{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Capstone_Project_Complete_Analysis",
      "provenance": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Raizel820/Raizel820-Capstone_Project_Machine_Learning/blob/master/Capstone_Project_Complete_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-xpifnZnQHZ",
        "colab_type": "code",
        "outputId": "f05f849a-f3ae-4a04-b630-4ba60012ae0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import subprocess as sp\n",
        "import sys,os,shutil,pickle,time\n",
        "from os.path import join\n",
        "# mount Google Drive\n",
        "from os.path import expanduser\n",
        "gd_path=join(expanduser(\"~\"),'gd')\n",
        "if not os.path.isdir(gd_path):\n",
        "    try:\n",
        "        # load Google Drive\n",
        "        from google.colab import drive,files\n",
        "        drive.mount('/drive')\n",
        "        sp.call('ln -s /drive/My\\ Drive '+gd_path, shell=True)\n",
        "    except:\n",
        "        print('unable to find Google Drive Folder')\n",
        "os.chdir(gd_path+'/MachineLearning/car_data')\n",
        "os.listdir('.')"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['craigslistVehicles_full.csv', 'cars_cleaned.csv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUwVqlALOez5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E_BsufYrOhqc",
        "colab_type": "code",
        "outputId": "30f24a6d-1006-4ead-d164-f60831607323",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df = pd.read_csv('cars_cleaned.csv')\n",
        "df.head(5)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>year</th>\n",
              "      <th>manufacturer</th>\n",
              "      <th>make</th>\n",
              "      <th>condition</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>fuel</th>\n",
              "      <th>odometer</th>\n",
              "      <th>title_status</th>\n",
              "      <th>transmission</th>\n",
              "      <th>drive</th>\n",
              "      <th>size</th>\n",
              "      <th>type</th>\n",
              "      <th>paint_color</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9000</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>suburban lt2</td>\n",
              "      <td>good</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>217743.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>SUV</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6000</td>\n",
              "      <td>2002.0</td>\n",
              "      <td>gmc</td>\n",
              "      <td>sierra 1500</td>\n",
              "      <td>good</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>195000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37000</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>3500</td>\n",
              "      <td>excellent</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>diesel</td>\n",
              "      <td>178000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3700</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>F150</td>\n",
              "      <td>fair</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>269000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19950</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>f-250</td>\n",
              "      <td>good</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>116792.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   price    year manufacturer  ...       size    type paint_color\n",
              "0   9000  2009.0    chevrolet  ...  full-size     SUV       white\n",
              "1   6000  2002.0          gmc  ...  full-size  pickup       white\n",
              "2  37000  2012.0    chevrolet  ...  full-size  pickup      silver\n",
              "3   3700  2003.0    chevrolet  ...  full-size  pickup      silver\n",
              "4  19950  2013.0         ford  ...  full-size  pickup       white\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YhsRnopOoxF",
        "colab_type": "code",
        "outputId": "abd35d9d-a019-423b-d69a-cf1e95f8cb89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 375332 entries, 0 to 375331\n",
            "Data columns (total 14 columns):\n",
            "price           375332 non-null int64\n",
            "year            375332 non-null float64\n",
            "manufacturer    375332 non-null object\n",
            "make            375332 non-null object\n",
            "condition       375332 non-null object\n",
            "cylinders       375332 non-null object\n",
            "fuel            375332 non-null object\n",
            "odometer        375332 non-null float64\n",
            "title_status    375332 non-null object\n",
            "transmission    375332 non-null object\n",
            "drive           375332 non-null object\n",
            "size            375332 non-null object\n",
            "type            375332 non-null object\n",
            "paint_color     375332 non-null object\n",
            "dtypes: float64(2), int64(1), object(11)\n",
            "memory usage: 40.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FmoH-BZlXzi2",
        "colab_type": "text"
      },
      "source": [
        "**Label Processing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BW-2vedgOqmI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import preprocessing\n",
        "import pandas as pd\n",
        "le = preprocessing.LabelEncoder()\n",
        "\n",
        "df[['manufacturer', 'make', 'condition', 'cylinders','fuel','title_status', 'transmission','drive','size','type','paint_color']] = df[['manufacturer', 'make', 'condition','cylinders', 'fuel','title_status', 'transmission','drive','size','type','paint_color']].apply(le.fit_transform)\n",
        "#df['city'] = le.fit(df['city'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f-KiBXM4X6Ig",
        "colab_type": "text"
      },
      "source": [
        "Split Train and Test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAeX7sagOw_k",
        "colab_type": "code",
        "outputId": "4a5b2789-ed9a-4511-c279-a4474c067e3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "from sklearn import datasets, linear_model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "y= df.price\n",
        "X= df.drop('price',axis=1)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "print (X_train.shape, y_train.shape)\n",
        "print (X_test.shape, y_test.shape)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(300265, 13) (300265,)\n",
            "(75067, 13) (75067,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k2GA08RjO9nG",
        "colab_type": "text"
      },
      "source": [
        "**Standard Scaler**: It assumes that your data is normally distributed within each feature. This is **not appropriate** for this data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ChKN9WaNO-VI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Feature Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "X_train= sc.fit_transform(X_train)\n",
        "X_test= sc.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jpt6_zuyKZy9",
        "colab_type": "text"
      },
      "source": [
        "**Min-max scaler:**\n",
        "\n",
        "For each value in a feature, MinMaxScaler subtracts the minimum value in the feature and then divides by the range. The range is the difference between the original maximum and original minimum."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qd7oncexeGhf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler()   \n",
        "X_train= sc.fit_transform(X_train)\n",
        "X_test= sc.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VedpRsH8PDlZ",
        "colab_type": "code",
        "outputId": "2e667469-408f-4ddb-afcc-45ea5f1a2274",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        }
      },
      "source": [
        "X_train"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.94117647, 0.85      , 0.01793642, ..., 0.33333333, 0.66666667,\n",
              "        0.81818182],\n",
              "       [0.76470588, 0.175     , 0.30864242, ..., 0.33333333, 0.16666667,\n",
              "        0.90909091],\n",
              "       [0.88235294, 0.4       , 0.33595876, ..., 0.33333333, 0.75      ,\n",
              "        0.72727273],\n",
              "       ...,\n",
              "       [0.5       , 0.575     , 0.44142919, ..., 0.66666667, 0.75      ,\n",
              "        0.45454545],\n",
              "       [0.58823529, 0.4       , 0.37985107, ..., 0.66666667, 0.        ,\n",
              "        0.81818182],\n",
              "       [0.85294118, 0.175     , 0.43706143, ..., 0.66666667, 0.        ,\n",
              "        0.72727273]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ISiQaY4OPQ-n",
        "colab_type": "text"
      },
      "source": [
        " **Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zrfdN0XvPGQU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "regressor = RandomForestRegressor(n_estimators=20, random_state=0)\n",
        "regressor.fit(X_train, y_train)\n",
        "y_pred = regressor.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cj-reN9WPYxz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import metrics\n",
        "from sklearn.metrics import mean_squared_error as MSE"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQzyt0QvPZfa",
        "colab_type": "code",
        "outputId": "1652d32a-7394-493e-e5c1-fa71b57c157e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "print('Mean Absolute Error:', round(metrics.mean_absolute_error(y_test, y_pred),2))\n",
        "print('Mean Squared Error:', round(metrics.mean_squared_error(y_test, y_pred),2))\n",
        "print('Root Mean Squared Error:', round(np.sqrt(metrics.mean_squared_error(y_test, y_pred)),2))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 2199.48\n",
            "Mean Squared Error: 16843482.83\n",
            "Root Mean Squared Error: 4104.08\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SiWE-en0PxrC",
        "colab_type": "text"
      },
      "source": [
        "Let's increase number of tress from 20 to 200"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RnqmopscP0zf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "regressor = RandomForestRegressor(n_estimators=200, random_state=0)\n",
        "regressor.fit(X_train, y_train)\n",
        "y_pred = regressor.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1u7Oo8Z5P4jd",
        "colab_type": "code",
        "outputId": "7f863eed-52d7-4e75-feff-6299891b8aa0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "print('Mean Absolute Error:', round(metrics.mean_absolute_error(y_test, y_pred),2))\n",
        "print('Mean Squared Error:', round(metrics.mean_squared_error(y_test, y_pred),2))\n",
        "print('Root Mean Squared Error:', round(np.sqrt(metrics.mean_squared_error(y_test, y_pred)),2))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 2122.92\n",
            "Mean Squared Error: 16014408.04\n",
            "Root Mean Squared Error: 4001.8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kwB94NYxP8M5",
        "colab_type": "text"
      },
      "source": [
        "With more trees, the model accuracy has increased slightly **(86.11% vs 86.79%)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6RL1RragQKU7",
        "colab_type": "text"
      },
      "source": [
        "Visulization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XCJ-SD1CP9ET",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Visualizing a Single Decision Tree\n",
        "\n",
        "# Import tools needed for visualization\n",
        "rf= regressor\n",
        "features= df.drop('price', axis = 1)\n",
        "\n",
        "# Saving feature names for later use\n",
        "feature_list = list(features.columns)\n",
        "# Convert to numpy array\n",
        "features = np.array(features)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6weEsv0VQzl9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import tools needed for visualization\n",
        "from sklearn.tree import export_graphviz\n",
        "import pydot\n",
        "\n",
        "# Limit depth of tree to 3 levels\n",
        "rf_small = RandomForestRegressor(n_estimators=10, max_depth = 3)\n",
        "rf_small.fit(X_train, y_train)\n",
        "# Extract the small tree\n",
        "tree_small = rf_small.estimators_[5]\n",
        "# Save the tree as a png image\n",
        "export_graphviz(tree_small, out_file = 'small_tree.dot',\n",
        "                feature_names = feature_list, rounded = True, precision = 1, proportion = False, filled= True)\n",
        "\n",
        "(graph, ) = pydot.graph_from_dot_file('small_tree.dot')\n",
        "graph.write_png('small_tree.png');\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vw6OnKBKSLho",
        "colab_type": "code",
        "outputId": "b2e0222b-6072-485b-a63c-22c7d28cec3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        }
      },
      "source": [
        "# Display in jupyter notebook\n",
        "from IPython.display import Image\n",
        "Image(filename = 'small_tree.png')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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jo1afGQAAAICH0NcDAACAjs7K2jrk9r0mG2o/aYjOgIH9Nbb5HS+r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\nuo57rSqOHhV6yO3F2/TRuhY8q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cyjJCn8rOgDvaubGcAYHlvb/NmCXaxHOXoz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mwWlf1iSs/GD8VnHXxVevBVKQDM1xY+tUAFAFZNEhPGc5xPqLY8k9HdNyDJh5kozbNtqrSc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\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H0tIyISGhoaVNw8DM6/zVru7u39MuxH9LV3e31/mrGgZmDS1tCQkJv7W/JSW3wEXn3cy9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\n+B8wqjnxqDPMLAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M9thVC6xTtY3",
        "colab_type": "text"
      },
      "source": [
        "**Variable Importance**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lkP1Rl_TWTL",
        "colab_type": "code",
        "outputId": "8637703f-2a20-40da-c6b5-12abfeb26eee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        }
      },
      "source": [
        "# Get numerical feature importances\n",
        "importances = list(rf.feature_importances_)\n",
        "\n",
        "# List of tuples with variable and importance\n",
        "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]\n",
        "\n",
        "# Sort the feature importances by most important first\n",
        "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n",
        "\n",
        "# Print out the feature and importances \n",
        "[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances];"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Variable: year                 Importance: 0.4\n",
            "Variable: odometer             Importance: 0.13\n",
            "Variable: make                 Importance: 0.11\n",
            "Variable: drive                Importance: 0.1\n",
            "Variable: fuel                 Importance: 0.09\n",
            "Variable: manufacturer         Importance: 0.05\n",
            "Variable: cylinders            Importance: 0.04\n",
            "Variable: type                 Importance: 0.02\n",
            "Variable: paint_color          Importance: 0.02\n",
            "Variable: condition            Importance: 0.01\n",
            "Variable: title_status         Importance: 0.01\n",
            "Variable: transmission         Importance: 0.01\n",
            "Variable: size                 Importance: 0.01\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rxcWjvsNT2Ij",
        "colab_type": "code",
        "outputId": "449485c0-49db-4021-f555-3ea5b950dfc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "# Set the style\n",
        "plt.style.use('fivethirtyeight')\n",
        "# list of x locations for plotting\n",
        "x_values = list(range(len(importances)))\n",
        "# Make a bar chart\n",
        "plt.bar(x_values, importances, orientation = 'vertical')\n",
        "# Tick labels for x axis\n",
        "plt.xticks(x_values, feature_list, rotation='vertical')\n",
        "# Axis labels and title\n",
        "plt.ylabel('Importance'); plt.xlabel('Variable'); plt.title('Variable Importances');"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Abt26BX9/f6Xj7ty5g3nz5mHUqFE4ffo05s2bh4CAAJw+fbpM8YmIqGJQK4HN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\niEL6P88Lc97USXoIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pyRc6P3eT5G7",
        "colab_type": "code",
        "outputId": "a01f101e-6123-41ea-d09c-68362f1cd743",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "# List of features sorted from most to least important\n",
        "sorted_importances = [importance[1] for importance in feature_importances]\n",
        "sorted_features = [importance[0] for importance in feature_importances]\n",
        "# Cumulative importances\n",
        "cumulative_importances = np.cumsum(sorted_importances)\n",
        "# Make a line graph\n",
        "plt.plot(x_values, cumulative_importances, 'g-')\n",
        "# Draw line at 90% of importance retained\n",
        "plt.hlines(y = 0.90, xmin=0, xmax=len(sorted_importances), color = 'r', linestyles = 'dashed')\n",
        "# Format x ticks and labels\n",
        "plt.xticks(x_values, sorted_features, rotation = 'vertical')\n",
        "# Axis labels and title\n",
        "plt.xlabel('Variable'); plt.ylabel('Cumulative Importance');\n",
        "plt.title('Cumulative Importances');"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CqpiK27B8TkVFBQCQlZXFaGD9+/fHxo0b4ejoCFNTU9y5cweRkZHo06dPpdv/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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLDmicr0UBWE",
        "colab_type": "code",
        "outputId": "8be000c5-39f3-4188-f9c9-ada1b2e9c591",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Find number of features for cumulative importance of 90%\n",
        "\n",
        "# Add 1 because Python is zero-indexed\n",
        "print('Number of features for 90% importance:', np.where(cumulative_importances > 0.90)[0][0] + 1)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of features for 90% importance: 7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jB31Ll8vUNZr",
        "colab_type": "text"
      },
      "source": [
        "New Random Forest with the most important seven features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ye1KJ39-UTBu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# New random forest with only the most important variables\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf_most_important = RandomForestRegressor(n_estimators=200, random_state=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUqFvaZPUXXS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Extract the most important features\n",
        "features= df.drop('price', axis = 1)\n",
        "\n",
        "# Saving feature names for later use\n",
        "feature_list = list(features.columns)\n",
        "# Convert to numpy array\n",
        "features = np.array(features)\n",
        "\n",
        "\n",
        "important_indices = [feature_list.index('year'), feature_list.index('drive'), \n",
        "                     feature_list.index('odometer'), feature_list.index('fuel'),\n",
        "                     feature_list.index('make'), feature_list.index('cylinders'), feature_list.index('manufacturer')]\n",
        "train_important = X_train[:, important_indices]\n",
        "test_important = X_test[:, important_indices]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HNrAZl6xUvWL",
        "colab_type": "code",
        "outputId": "90dd1876-cab3-46fe-b8c6-e79d81b3f588",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "# Train the random forest\n",
        "rf_most_important.fit(train_important, y_train)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
              "                      max_features='auto', max_leaf_nodes=None,\n",
              "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                      min_samples_leaf=1, min_samples_split=2,\n",
              "                      min_weight_fraction_leaf=0.0, n_estimators=200,\n",
              "                      n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
              "                      warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CwT3LdJMU7cW",
        "colab_type": "text"
      },
      "source": [
        "Make pedictions with this filtered model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOpjzxtbUyX0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = rf_most_important.predict(test_important)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_o2IvK_OU5td",
        "colab_type": "code",
        "outputId": "a2c14b9e-049b-47fd-ebf4-a0da2a47c502",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "from sklearn import metrics\n",
        "print('Mean Absolute Error:', round(metrics.mean_absolute_error(y_test, predictions),2))\n",
        "print('Mean Squared Error:', round(metrics.mean_squared_error(y_test, predictions),2))\n",
        "print('Root Mean Squared Error:', round(np.sqrt(metrics.mean_squared_error(y_test, predictions)),2))"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 2047.74\n",
            "Mean Squared Error: 15682494.73\n",
            "Root Mean Squared Error: 3960.11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-xZhVhctVF3e",
        "colab_type": "text"
      },
      "source": [
        "This accuracy is slightly better than the full model (86.79 % vs 87.07). In addition, we this accuracy was obtained just by using 7 features instead of 13. Therefore, it can be considered as an improvement in both accuracy and efficiency."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-kp1XhPOVgv5",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YHbNFUrOViv0",
        "colab_type": "text"
      },
      "source": [
        "# Ridge Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7BGv5LAVcTZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import model_selection\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.linear_model import ElasticNet\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajafQfwfVrG7",
        "colab_type": "text"
      },
      "source": [
        "First, linear regression will be applied\n",
        "###Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fiofC-JzlgC_",
        "colab_type": "code",
        "outputId": "b43bb766-d2eb-4afb-8161-8b26d8d143aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "from sklearn.linear_model import LinearRegression, Ridge\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train, y_train)\n",
        "y_pred_lr = lr.predict(X_test)\n",
        "rmse_lr = np.sqrt(mse(y_test, y_pred_lr))\n",
        "rr = Ridge()\n",
        "rr.fit(X_train, y_train)\n",
        "y_pred_rr = rr.predict(X_test)\n",
        "rmse_rr = np.sqrt(mse(y_test, y_pred_rr))\n",
        "print('-------------Linear Regression-------------')\n",
        "print(\"RMSE = {:.2f}\".format((rmse_lr)))\n",
        "accuracy = lr.score(X_test,y_test)\n",
        "print('Accuracy = ', accuracy*100,'%')\n",
        "print('-------------Ridge Regression--------------')\n",
        "print(\"RMSE = {:.2f}\".format((rmse_rr)))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-------------Linear Regression-------------\n",
            "RMSE = 7578.77\n",
            "Accuracy =  52.62602873481181 %\n",
            "-------------Ridge Regression--------------\n",
            "RMSE = 7578.77\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kmfzjOwGVoSg",
        "colab_type": "code",
        "outputId": "55e08164-f7c5-4b37-d724-7f8fff9aee25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "regressor = LinearRegression()  \n",
        "regressor.fit(X_train, y_train) #training the algorithm\n",
        "\n",
        "#To retrieve the intercept:\n",
        "print(regressor.intercept_)#For retrieving the slope:\n",
        "print(regressor.coef_)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5065.034597901995\n",
            "[ 24408.0009971    -686.18389293     42.93591976   1207.00656648\n",
            "  12025.87380365 -16482.15731238 -20652.8980096   -5503.7893133\n",
            "   1240.99082884  -3997.91777084    -49.60690397    403.29977124\n",
            "    410.79072931]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zDx-vhV-V_kF",
        "colab_type": "code",
        "outputId": "4e18dff6-7afb-4eb5-d933-b7649bbc4eba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        }
      },
      "source": [
        "coeff_df = pd.DataFrame(regressor.coef_, X.columns, columns=['Coefficient'])  \n",
        "coeff_df"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>24408.000997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manufacturer</th>\n",
              "      <td>-686.183893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make</th>\n",
              "      <td>42.935920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>condition</th>\n",
              "      <td>1207.006566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cylinders</th>\n",
              "      <td>12025.873804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel</th>\n",
              "      <td>-16482.157312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>odometer</th>\n",
              "      <td>-20652.898010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_status</th>\n",
              "      <td>-5503.789313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>transmission</th>\n",
              "      <td>1240.990829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>drive</th>\n",
              "      <td>-3997.917771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>size</th>\n",
              "      <td>-49.606904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <td>403.299771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>paint_color</th>\n",
              "      <td>410.790729</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Coefficient\n",
              "year          24408.000997\n",
              "manufacturer   -686.183893\n",
              "make             42.935920\n",
              "condition      1207.006566\n",
              "cylinders     12025.873804\n",
              "fuel         -16482.157312\n",
              "odometer     -20652.898010\n",
              "title_status  -5503.789313\n",
              "transmission   1240.990829\n",
              "drive         -3997.917771\n",
              "size            -49.606904\n",
              "type            403.299771\n",
              "paint_color     410.790729"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kkEba3oiW4OK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "OLS_pred= regressor.predict(X_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzTZcajWXFzV",
        "colab_type": "code",
        "outputId": "f9d2f15a-637b-4d77-d234-335f31cf33c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "from sklearn import metrics\n",
        "print('Mean Absolute Error:', round(metrics.mean_absolute_error(y_train, OLS_pred),2))\n",
        "print('Mean Squared Error:', round(metrics.mean_squared_error(y_train, OLS_pred),2))\n",
        "print('Root Mean Squared Error:', round(np.sqrt(metrics.mean_squared_error(y_train, OLS_pred)),2))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 5398.06\n",
            "Mean Squared Error: 56888661.51\n",
            "Root Mean Squared Error: 7542.46\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GVVjh50KY764",
        "colab_type": "text"
      },
      "source": [
        "Check performance of the OLS on test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BpTPWXkeZD_w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = regressor.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlyNVC5QXRIP",
        "colab_type": "code",
        "outputId": "b15ce48b-03bd-4674-c57d-8b77e05611e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 834
        }
      },
      "source": [
        "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
        "df1 = df.head(25)\n",
        "round(df1,2)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>23967</th>\n",
              "      <td>5490</td>\n",
              "      <td>7011.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252949</th>\n",
              "      <td>19988</td>\n",
              "      <td>3791.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318549</th>\n",
              "      <td>1250</td>\n",
              "      <td>-268.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>375312</th>\n",
              "      <td>3950</td>\n",
              "      <td>11863.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>317221</th>\n",
              "      <td>9000</td>\n",
              "      <td>5877.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228400</th>\n",
              "      <td>15900</td>\n",
              "      <td>18694.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211614</th>\n",
              "      <td>57000</td>\n",
              "      <td>26481.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39076</th>\n",
              "      <td>27989</td>\n",
              "      <td>23169.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>221969</th>\n",
              "      <td>19900</td>\n",
              "      <td>23538.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>326142</th>\n",
              "      <td>14799</td>\n",
              "      <td>9801.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227254</th>\n",
              "      <td>26400</td>\n",
              "      <td>24521.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55060</th>\n",
              "      <td>16399</td>\n",
              "      <td>17330.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>245543</th>\n",
              "      <td>24500</td>\n",
              "      <td>22698.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152719</th>\n",
              "      <td>10999</td>\n",
              "      <td>12157.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117588</th>\n",
              "      <td>4490</td>\n",
              "      <td>13228.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66641</th>\n",
              "      <td>14000</td>\n",
              "      <td>20455.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307610</th>\n",
              "      <td>11895</td>\n",
              "      <td>12537.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209554</th>\n",
              "      <td>3650</td>\n",
              "      <td>-4664.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54172</th>\n",
              "      <td>9999</td>\n",
              "      <td>2418.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28839</th>\n",
              "      <td>4200</td>\n",
              "      <td>11021.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72869</th>\n",
              "      <td>1000</td>\n",
              "      <td>4362.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27416</th>\n",
              "      <td>24900</td>\n",
              "      <td>28921.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173743</th>\n",
              "      <td>14775</td>\n",
              "      <td>21583.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165424</th>\n",
              "      <td>12832</td>\n",
              "      <td>16747.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300271</th>\n",
              "      <td>8900</td>\n",
              "      <td>2586.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Actual  Predicted\n",
              "23967     5490    7011.43\n",
              "252949   19988    3791.14\n",
              "318549    1250    -268.05\n",
              "375312    3950   11863.52\n",
              "317221    9000    5877.70\n",
              "228400   15900   18694.98\n",
              "211614   57000   26481.44\n",
              "39076    27989   23169.30\n",
              "221969   19900   23538.65\n",
              "326142   14799    9801.11\n",
              "227254   26400   24521.26\n",
              "55060    16399   17330.38\n",
              "245543   24500   22698.53\n",
              "152719   10999   12157.11\n",
              "117588    4490   13228.41\n",
              "66641    14000   20455.81\n",
              "307610   11895   12537.59\n",
              "209554    3650   -4664.00\n",
              "54172     9999    2418.70\n",
              "28839     4200   11021.36\n",
              "72869     1000    4362.14\n",
              "27416    24900   28921.45\n",
              "173743   14775   21583.63\n",
              "165424   12832   16747.55\n",
              "300271    8900    2586.01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGy5veVRXcbR",
        "colab_type": "code",
        "outputId": "33ff92df-482f-454c-e110-d60c6e27de4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        }
      },
      "source": [
        "df1.plot(kind='bar',figsize=(10,8))\n",
        "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
        "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FaWlpV3ywrHnz5nXauzIEWAAAgDp266236s4776x2/T333KOcnBw9FvGYfFpcuap61113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\nhAgRIkRIi+L/ADdyVHg9aL49AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5KtRzmlMYqza",
        "colab_type": "text"
      },
      "source": [
        "Evaluate OLS prediction on test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZsnkpvuYw-b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = regressor.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dPPVrnAsY08c",
        "colab_type": "code",
        "outputId": "f2e28349-4a6f-4421-d24a-5c44728fd5b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "print('Mean Absolute Error:', round(metrics.mean_absolute_error(y_test, y_pred),2))\n",
        "print('Mean Squared Error:', round(metrics.mean_squared_error(y_test, y_pred),2))\n",
        "print('Root Mean Squared Error:', round(np.sqrt(metrics.mean_squared_error(y_test, y_pred)),2))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 5406.23\n",
            "Mean Squared Error: 57437756.47\n",
            "Root Mean Squared Error: 7578.77\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YRRmLOtZWrS",
        "colab_type": "text"
      },
      "source": [
        "### Ridge Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGE_ygtuZdj4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "alphas = 10**np.linspace(10,-2,100)*0.5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-FIvUPncVOs",
        "colab_type": "code",
        "outputId": "00c54ed1-40db-4b61-e898-ca2535232a78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from mpl_toolkits import mplot3d\n",
        "from sklearn.preprocessing import scale \n",
        "\n",
        "%matplotlib inline\n",
        "plt.style.use('seaborn-white')\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.datasets import make_regression\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "clf= Ridge()\n",
        "coefs = []\n",
        "\n",
        "for a in alphas:\n",
        "    clf.set_params(alpha=a)\n",
        "    clf.fit(scale(X_train), y_train)\n",
        "    coefs.append(clf.coef_)\n",
        "    \n",
        "ax = plt.gca()\n",
        "ax.plot(alphas*2, coefs)\n",
        "ax.set_xscale('log')\n",
        "plt.axis('tight')\n",
        "plt.xlabel('alpha')\n",
        "plt.ylabel('weights')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'weights')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CILSIJEkNzVFhUeazHqtpOjXlLsoL6ijNr6Ekr4binGqyt5cAYDDKJPWJouvAGLqmxhAa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\nOz81Bb6xuQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KzuyW4T7cbwI",
        "colab_type": "code",
        "outputId": "5aa5f342-0954-4cfc-ae24-698f407eedb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from sklearn.preprocessing import scale \n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import Ridge, RidgeCV, Lasso, LassoCV\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "ridgecv = RidgeCV(alphas = alphas, scoring = 'neg_mean_squared_error', normalize = True)\n",
        "ridgecv.fit(X_train, y_train)\n",
        "ridgecv.alpha_"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.005"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_mOdCy-cid-",
        "colab_type": "text"
      },
      "source": [
        "Finally, we refit our ridge regression model on the full data set, using the value of alpha chosen by cross-validation, and examine the coefficient estimates."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yWXQAIGci9-",
        "colab_type": "code",
        "outputId": "3cead7fa-d845-467c-dfab-f63d83367995",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        }
      },
      "source": [
        "# Fit a ridge regression on the training data\n",
        "pred2 = ridgecv.predict(X_test)\n",
        "# Use this model to predict the test data\n",
        "print(pd.Series(ridgecv.coef_, index = X.columns))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "year            24330.482158\n",
            "manufacturer     -685.577286\n",
            "make               41.250393\n",
            "condition        1203.802948\n",
            "cylinders       11962.323294\n",
            "fuel           -16392.010951\n",
            "odometer       -20575.757009\n",
            "title_status    -5473.016577\n",
            "transmission     1236.703486\n",
            "drive           -3984.666869\n",
            "size              -51.187410\n",
            "type              405.110962\n",
            "paint_color       409.573864\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IoeATuwAbBqb",
        "colab_type": "code",
        "outputId": "4ddd5ef9-1cbe-4f8e-a11a-23c9748329ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "from sklearn import metrics\n",
        "print('Mean Absolute Error:', round(metrics.mean_absolute_error(y_test, pred2),2))\n",
        "print('Mean Squared Error:', round(metrics.mean_squared_error(y_test, pred2),2))\n",
        "print('Root Mean Squared Error:', round(np.sqrt(metrics.mean_squared_error(y_test, pred2)),2))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 5405.78\n",
            "Mean Squared Error: 57436987.51\n",
            "Root Mean Squared Error: 7578.72\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NhM1swcecraR",
        "colab_type": "code",
        "outputId": "89bfcc1d-d6ee-4f68-8e4a-d5580a81d16e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "coef_ridge= pd.Series(ridgecv.coef_, index = X.columns)\n",
        "imp_coef = coef_ridge.sort_values()\n",
        "import matplotlib\n",
        "matplotlib.rcParams['figure.figsize'] = (7.0, 7.0)\n",
        "imp_coef.plot(kind = \"barh\")\n",
        "plt.title(\"Feature importance using Ridge Model\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Feature importance using Ridge Model')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EROROHvgFSF5eXn+4fteuXVy5coV69eo9oIpERETMybcwjYqK4ttvvyUtLY2z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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7QYDgxIed2IY",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zYAO412ld9VK",
        "colab_type": "text"
      },
      "source": [
        "## **LASSO**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rryiwYnd8dv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import Lasso\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from mpl_toolkits import mplot3d\n",
        "from sklearn import linear_model\n",
        "from sklearn import datasets\n",
        "from sklearn.preprocessing import scale \n",
        "\n",
        "%matplotlib inline\n",
        "plt.style.use('seaborn-white')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y9O9ldird24H",
        "colab_type": "code",
        "outputId": "370050a6-18e2-45f9-ace6-0426ebf3dab8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        }
      },
      "source": [
        "lasso = Lasso(max_iter = 10000, normalize = True)\n",
        "coefs = []\n",
        "alphas= np.logspace(-1,2,100)\n",
        "for a in alphas:\n",
        "    lasso.set_params(alpha=a)\n",
        "    lasso.fit(scale(X_train), y_train)\n",
        "    coefs.append(lasso.coef_)\n",
        "    \n",
        "ax = plt.gca()\n",
        "ax.plot(alphas*2, coefs)\n",
        "ax.set_xscale('log')\n",
        "plt.axis('tight')\n",
        "plt.xlabel('lambda')\n",
        "plt.ylabel('weights')\n",
        "plt.title('Lasso Paths')\n",
        "plt.legend()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fee3a1ee908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vvfUWAIWFhUyaNImJEydy++23EwwGAfjkk0+49NJLGTduHO+//z4AoVCIqVOn\nMmHCBK666ioOHRK/doT6I8sSXQdnc+UjZ9FjaA5bVxzm7T8376opSZL482+7UeEN8twXOxMdjpAg\ncU8WXq+Xv/71rwwcOLC2bMaMGUycOJG3336b1q1bM2/ePLxeLzNnzmT27Nm8+eabzJkzh4qKChYs\nWIDdbuedd95h8uTJPPPMM/F+CkIzYDCrDLm8I+Pu64fNYWTJ61tY8OJG3GX+RIeWEN2yk7iif0ve\nWn2AI5XN8zVo7uKeLPR6Pf/85z9xOp21ZWvWrGHYsGEADB06lFWrVrFx40Z69OiBzWbDaDTSp08f\n8vLyWLVqFcOHDwdic3fn5eXF+ykIzUh6SxuX3tOPIVd0oHB3Je/8ZQ1bvzncLM8yppzXnqgGry3f\nm+hQhASIe7LQ6XQYjceOCurz+dDrY5PapKam4nK5KCkpweH4YWY1h8NxXLksy0iSVFttJQingyxL\n9BzakvEPnomzlY1lb25nwYvfU13ZvC5Wa+kw89te2by99iDl1eIz19w0uAbuE/1iO9VyQahv9jQT\nF99xBkOu6MjhneW8+9e17N9Ukuiw4urm89rhDUaYvXJ/okMR4qxBJAuz2YzfH6sHLSoqwul04nQ6\nKSn54YNYXFxcW+5yuYBYY7emabVnJYJwukmyRM+hOVx+f38syQY+nfk9y+fuJByKJDq0uOiYYWN4\n1wxmr9xPdUBc9d6cNIhkMWjQIBYvXgzAkiVLGDJkCL169WLTpk1UVVVRXV1NXl4e/fr1Y/DgwSxa\ntAiAZcuWMWDAgESGLjRTKZkWLrunLz3Pz+H7Zfl8OD2PqlJfosOKiynntaPSF+KdtQcTHYoQR3Ef\ndnPz5s08+eSTFBQUoNPpWLx4MU8//TT33nsvc+fOJTs7m7Fjx6KqKlOnTuW6665DkiRuueUWbDYb\no0ePZuXKlUyYMAG9Xs8TTzwR76cgCADoVIUhl3ekRccUls7ZxnuPrePCa7vRqltqokM7rc5olcLA\n3FReW76P3w9uiyKLIcybA0lrYpX++fn5DBs2jKVLl5KTk5PocIRmoqLYy6J/bKb0sIcBF+XSd1Tr\nJj0PxCcbD/OHdzYwb/JA+rVx1H0HocGr67uzQVRDCUJjl+w0c+k9fenYP4M1n+zl81lbCQebbjvG\n0E7p6BWZxVuOJDoUIU5EshCEeqLqFS74fVfOGpvLrnVFfPTshibbvdZmVBnUPpXFW5rvle3NTZ3J\nYtu2baxYsQKAmTNnMmXKFL799tvTHpggNEaSJNF3ZBtGTe5BWWE1Hzz5LeVHqhMd1mkxolsmB8u8\nbD8iBlxsDupMFo888ght2hfup5kAACAASURBVLThm2++Yfv27Tz88MO88MIL8YhNEBqt3N7p/G5q\nH8KhCB9M/5bCPZWJDqneXdAlA0lCVEU1E3UmC71eT05ODp9//jkTJkwgIyODaFRMECMIdUlvZePS\nu/thNKt8/NwG9m10JTqkepVuM9CvdQqLtxQlOhQhDupMFqqq8sADD7B+/XoGDBjA119/TTgsLsYR\nhJORlG7i0rv7kpptYdE/NrMnrzjRIdWrEd0y2VZYxcFSb6JDEU6zOpPF888/z7nnnssbb7yBoiio\nqsrTTz8dj9gEoUkw2fRcfMcZONvYWfzaFnatbzq/xEd0ywREVVRzUGeymDZtGsOHDyc9PR2AgQMH\ncscdd5z2wAShKdGbdFz0h15k5tr5fNZWdq1rGgmjpcNMlyy7SBbNwAmv4F68eDGvvvoqO3bsYODA\ngbXd4zRNo0uXLnELUBCaCr1Rx5hbe/HpzO/5/I2tqEaFNj3SEh3WrzaiWwbPL91FiSdAmtWQ6HCE\n0+SEZxYjRozggw8+4I9//COrVq1i9erVrF69mjVr1jB79uw4higITYfeqOM3U3qSlmNl0aubObyr\nItEh/WoXdMlA0+DLHU2rAV84Vp1jQw0aNIjHH38ct9t9zMU3jz/++GkNLBHC5eVUzv8YAMmgR9br\nkQwGJL0htm8wxPYNBmSjEclgRDYakIym2FoX96G2hEZIb9Jx0W29+PDpPD6duZGxf+pDeitbosP6\nxbpl28mwG1i2vZjL+oohdpqqOr/d7rrrLiZNmkRGRkY84kko/9atFD/9NER+2TANkqoimUyxRGIy\nIhtNyGYzssmEbDEjm81I5thatlhQLBZkiwXZao2tLVYUmxXZZkO22pAt5iY9vlBzZrLp+e3tvflw\n+rf858WNXHZ3X+xppkSH9YtIksT5nZ0s2FhIMBxFrxMDQzRFdSaLzMxMrrjiinjEknDWwYPptCEP\nLRBACwbR/H6iwSBaMIQW8KMFAkQDQbSAn6jfj+YPEA3UrP0+NN/Rch9Rr4+oz0fU5yVaXU24pISo\nN7Yd9XrRAicxDIQso9hsyHZ7bJ1kR0lKRklKii3JybElJRldSgqKw4HiSBVJppGwOYxc9IdYwlgw\n83suvasPBrOa6LB+kaGdnLyz9hDr95cxqH3jb4cRjnfCZPHVV18B0KFDB5566in69u2L7kfVLOee\ne+7pjy4BZL0e4jCZkhYK1SaPiMdD1OOJJRK3m4jbQ9TjJuJ2E62KrSNVlUQrqwgc2UGkqopIZSWc\n4HoXyWBASXWgS01Dl1azpKehpKWhOp3oMjLQOZ3o0tKQFOW0P1fhxBxZFkbe1IP/zPiOz/6xmYtu\n64XSCH+ZD26fhl4ns3R7sUgWTdQJk8XRCYaO+uKLL47Zb4rJorJqI9u23QNIKLIRWTEiy4aabVNt\nmSKbYvuKCUUxo8g166P7Ois6xVxTZkWWDcf90pdUtfYM4Zf8ltQ0jajHQ6Sigkh5OeGyMiJl5UTK\nywiXlhEpLSFcWkaosBDf5k1ESsvgf6+8VxR06emoGRnosrNQs7JRs7NRW2Sjz8lBzclBNjXOqpHG\nJKdTCkMndWbp7G18+e/tnH91l0Z3Zmgx6BiYm8qy7cU8OKZrosMRToMTJoum2IBdF72ags3WnUik\nmmjETyQaIByuIhgpJhL1EY0EiER9RCI+NC100seVJB2KYkWns6JTLCg6W2xbZ0Ons6PT2VGPbqtJ\nqLokVDUZnS4ZVU1GUY6vVpIkCcVmQ7HZoGXLOmPQwmHCZWWEi12Ei4sJFxcRKioiXHiE0JEjBLZu\nw7P0v2jB4DH3U9LT0Ldqjb5lS/RtWqNv0xZ92zboW7dGNhpP+jUQfl7ns7KodPlY/+l+0nJs9BpW\n9/+0oRnWxclDH29hr8tDbro10eEI9azONotzzz0Xl8uFoihIkkQkEiE5OZmkpCSmTZvG2WefHY84\nj/HYY4+xceNGJEli2rRp9OzZs16OW+wP8daBMgAMiglVl4xRZ0KvM2FULehNZox6GwbVgl5RMcgy\nqgT6mkWVIuglDZUIKkHQAkTC1YQjHiIRD+Gwh3DYTSTsIRh04fXurSmrQtNOPISKJOlR1WT0agqq\nmoKqd6BXU1H1qehrlzQM+nT0+nQU5fizAUmnQ3U6UZ1OoNtPPo6maURKSwkVFBA8lE8oP5/goYOE\nDhykeuVKKufP/3FQqDk5GHJz0bdvh6FDB4wdO6LPzRVJ5Bc68zdtKc338M0Hu3G0sNCyc+OaVGho\nJyewhf9uLxbJogmqM1mMGjWKs846q7baacWKFeTl5TF+/Hhuu+22uCeLtWvXcuDAAebOncuePXuY\nNm0ac+fOrZdje3YtYuehb/DKEkFJwi/F1gFJQvsF1QIqEmZJh1nWY1b0WBQjJp0Ri86CVZ+BWd8e\nqyEJq92ByWjDrFMxyRImScMoRzASxEAAOeImHK4kFConGCrD49lOMFhKOPzTI5kqihWDIQODwRlb\n6zNq9jMxGrMwGDLR69OQpGPrxiVJqm3jMPXqddxxo9XVBA8eJLhvH4G9+wju3UNgz16qV65EC9Wc\nacky+rZtMXbujLFLZ4xdu2Ls2hUlOfmUX7/mRpIlLvh9V+Y9+S1L/rmFcff1a1Q9pFo6zHTMsLJs\nRzHXD8lNdDhCPaszWXz33Xfce++9tftDhgzhlVde4fbbb09IveqqVau44IILAGjXrh2VlZV4PB6s\n1l//S6Zb/yl8kDMYgtUQ9tcuWshPKOghEPISCHnwh7wEQtX4Q158IS+BiA9/yIc34scX9uOLBvFF\nQvi0AN5oNV6i+GSZakmiWpYpkyWqJRmPHNuP1PE6qkgkyQaSdGaSVAtJqoMUQ1uSDEkkKSp2RcJK\nFDnqh4iXcNiDN1BFOHSIUHgz4bAbTTu2vUJCRqdYUBUbesWGTraiV6zoFBuKVEcrihHoWrOQi6a1\nJeKuJlJZRbi8inBFOZGyxURXfwSrY3eRLRZ0acnoUlPQOZJAlmvikCDObyNN0whFNYLhxMxk55FU\nqqQTd6IIt4WiEpUZb83FZIii6qOoisb/vk00DUKRECRiEGgNJO34hvjzlTC+qgiPPf8yOkVGlRtf\nY31jZpCN/H7SU5gslno/9kl1nb3lllvo06cPsiyzadMmLBYLS5YsITs7u94DqktJSQnduv1QjeJw\nOHC5XPWSLB5YtIAF35We4FZDzZJyysdVtAhmApgJYNECmPGTTgCr5sOMHxNeVKkanexFkfzIsh9N\nChCVA0TkMEE5jF92U6XIVMgy+YrMZlmmXFEIn1TCNp+gXAOqapZ6klKztP3fx9WA8tjSNCePq1+p\nJ/E3EifxCY4zUQOZcDv+MZnn//RmvR+3zrfa008/zfLly9mzZw/hcJgRI0YwdOhQfD4f559/fr0H\ndKoaw5SOEUnBjRk35h/9iq6JW9Ni25oGUe3YfaKxtaahaGFs+LDjw6wFaEOQzgSISBFCSoiQEkKT\nNKISaHKUH0ZykWLLSf56V6TT+zNVioJc8xTjRwaOdhGueV1rt0+/sKTDq7MSRULRoqjRIAonOcy/\nBj/1z5M0Yu8NKf5nZhIyCgpRomgJOa0RTiQS1WPqNPS0HPuEyeKLL77gggsuqG0PMNV0oSwpKeH9\n99/nyiuvPC0B1cXpdFJSUlK7X1xcXDsi7q/16KiLeHTUT9wQCUHIV7N4f1gHq2PbQU/Nvje2HfTE\nbgt4IOiOrQPumqUK/FUQqmOqTVkFswNMKWBygCUVzEeXTLCkxbatTrCkx7aVxnlBV32LRqPs2rWL\ndevWsXv3bmRZpkePHgwcOJDMzMz4xaFp/H1/EU/vP0JHi5HnO7eil830q6pvyw4X8P5fpxGormbU\nbVPp0H9gPUZct2CBh+KXvsPYMYXUq7s2ui6+wi93wmThdsfm1S0vL49bMCdj8ODBvPDCC4wfP54t\nW7bgdDrrpQoKgMMbYP4tsS/0kA/CgVgS0E6xblvWgd4CBjvorWCwgjEJklqAwQaGJDDaY7cbk8CU\nHFsbk2qSQwqoZo6rpBZ+VmVlJRs2bGDDhg1UVlZitVo577zz6Nu3LzZbfMdecgVD/GHbQZaVubks\nI4UnO+Vg+ZUXQEajERa99CzhYJDxf3kKZ5v4NiJHAxHK3tmObFFJuayjSBTNzAmTxSWXXALArbfe\nypEjR8jPz6dfv34Eg0H0cbjC+UT69OlDt27dGD9+PJIk8fDDD9ffwQ12yOwR+5LWGUE1HbvWm2Nf\n4jpjLBmo5poyS2z/6KITwzTHSzAYZPv27WzcuJE9e/YAkJuby4UXXkjnzp1REnCF+uclldyx/RDV\nkQjTO+VwVVZqvXyxbvhsAYW7djD61qlxTxQAlQv3Ei71kXZ9DxSLOIttbupss5g9ezaLFi3C5/Px\n8ccfM336dJxOJzfccEM84vtJd9555+k5cGo7+N0/Ts+xhXoTDofZu3cvmzdvZvv27QSDQZKSkjjn\nnHM444wzSEk59U4I9cEbifLonsPMKiihq8XIy93a08lSPy2+FUcKWfHuv8jt05/OZ59XL8c8FcEC\nD9Vrj2AdlI2xnegG3RzVmSy++OIL3n33XSZNmgTEZs4bP358QpOF0PyEQiH27t3Ltm3b2L59O36/\nH6PRSLdu3ejVqxetWrVCTmA3zdUVHu7YfpD9viA35qQzLTcLo1I/8WiaxpJXX0BWFC64/pa4V/9o\nmkbFJ3uQzSr2C1rH9bGFhqPOZBGpGa776Bs0EAgQPsEAdoJQnzweD7t372bnzp3s2rWLUCiEwWCg\nU6dOdO/endzc3GMGt0yE6nCEJ/cd4Z/5Lloa9XzQux2DU+q3fWTj559xaMv3DL/xVmyp8R+kz7fR\nRfBAFSmXdkA2NbS+ukK81PmfHzx4MNdccw0HDx7k4YcfZvXq1VxzzTXxiE1oZiKRCAUFBezZs4fd\nu3dTUFAAgNVqpWfPnnTp0oU2bdokPEEctbikkmk78ykIhPi/Fmk8mJuFRVe/bSSlBYf46s3Xad3z\nDHqcP6Jej30yooEIFQv3obawYu7b9Oe0EU6szk/dhg0b2LJlC0lJSfTt25fJkyeTlZUVj9iEJi4a\njVJcXMy+ffvYt28fBw4cIBAIIEkSLVq0YOjQoXTs2JHMzMwG1fPmoC/Aw7sP81lJJZ0tRj7p2poz\nk+t/LKRIOMTCF55GZzAw8uY7EvIauJcdJFoVJPXKLkhyw/kfCPFXZ7J4/fXX0TSNHTt2sGHDBh54\n4AEKCgqOG8JcEOoSCoU4fPgwhw4d4sCBAxw6dAi/3w/ErsTv1q0b7dq1Izc3t/a6noakOhLhxQPF\nvHyoGAm4PzeLyS2dqKfpS3Tle/+meN8efjt1GlbHyVzSXb9CRdW4vy7A3MeJobU97o8vNCx1Jost\nW7bw3XffsXHjRqqqqsjOzmbkyJHxiE1oxKLRKCUlJRQUFHD48GEKCgo4cuQI0Zo5NVJTU+natSut\nWrWibdu2JCUlJTjiE4toGvOOlPPkvkIOB0Jc4kzmgXbZtDCevi7kh7Z8z9pPPqD70AvpcOag0/Y4\nJ6JFNco/3I1sVEj6jRgUUDiJZDFp0iR69OjBpEmTGDRoEGbzicYZEpqrYDBIcXExRUVFHDlyhMLC\nQoqKigjVjESr1+vJyspi0KBB5OTkkJOTU38XUp5Gmqbx3zI3j+45zLZqP71sJl7q2pqzTkOV049V\nlbhY8PxTpGRmM/T/EtPrsHrdkVij9mUdxTUVAnASyWLdunVs3bqVvLw8HnzwQdxuNy1atKjfi+GE\nRiEQCFBaWkpJSQkul4vi4mJcLhdlZWW1f6PX68nMzKRPnz5kZWWRnZ1NWlpaQru1nipN01he7mH6\nviOsq6qmjUnPP7q15qL0ZOTT3G4QDgb5z7OPEQoEuPyhx9Ab418dF3EHqfxsP4bcJMx9nXF/fKFh\nqjNZyLKMXq/HaDSi1+sJhUK1Q4EITU8gEKC8vJzy8nLKysooKyujtLSUsrIyqqp+GJ1WlmUcDgeZ\nmZn07NmTjIwMMjIySE5OblSJ4cc0TeOrcjfP7S9idWU12QaVJzrmMDHLgT4Oz0nTNL54bSZH9uzi\nt3feT2pOq9P+mD+l4j970EIRki9p36A6FgiJVWeyGD16NN27d+fMM8/kpptuok2bNnEISzgdotEo\n1dXVVFVVUVVVRWVlZe1SUVFBRUUFXq/3mPuYTCZSU1Np27YtqamppKWlkZaWhsPhaDBdWH+tiKbx\nqauSFw8U8b3HR6Ze5bEOLbgyOxVDHBPfhkX/YctXSznr0glxHyDwKO8mF77vS7APb42aLqqchR/U\n+Wn/7LPP4hGH8AtpmkYwGMTr9eLxeKiurqa6uhqPx4Pb7a5dH90+2sB8lE6nIykpieTkZLKyskhO\nTiYlJYWUlBQcDkeD7JVUX9zhCO8UlvJafgkH/UFyTQae7dSSSzNT4pokAPZ8u5Yv57xGu35nMeiy\nCXF97KMiniAV83ejtrBiOy8nITEIDVfT+GnYBGiaRigUwu/31y4+n6927fP58Hq9tesfLye6ot5k\nMmG1WrHZbKSlpWGz2bDb7bVLUlISZrO52VU1bK/28WZBKXOPlOGJRDkzycJD7bIZlZ6EkoDXomjf\nHj59/inS27TlN7fdiZSAajxN06iYv5uoP0L65R2R6mmoEqHpEMniF9A0jUgkQigUOmYJBoPHrY8u\ngUDgmO2ji9/vr93+31/9/8toNGIymTCbzdhsNjIyMjCbzVgslmMWq9WKxWJpMtVE9cEbibLQVcFb\nh0tZXVmNXpIY40zmhpx0zrAnrrrFXVrC/CcfwWi1ccndD6EaEzPVnO97F77NpdhHtkHNqP8pOYXG\nT3yb/EhpaSlLly4lGAwSDocJh8OEQqHjtkOh0CnP0KfT6TAYDMd0FrDb7aSnp2M0GjEYDBgMBkwm\nE0ajsTYx/HjdWBuOEyWqaayprOb9I2V8UlyBJxKljUnPg+2yuSLTQZo+sW9/v8fDh0/8maDfx/hH\nnkrIhXcA4YoA5fP3oG9pwzZEVD8JP00kix8JBAJUVFQgSRKKoqDX62t/oet0OlRVRVXV47b1en3t\nvl6vr90/uq3X68UXfZxomsb3Hh/zi8r5uLiCw4EQZkXmt+nJXJHl4KwkS4OodgsF/Hz05COUH87n\nknv+THrrtgmJQ4tqlM3dAZEojis6ISmJf22EhinuyWLt2rXcfvvtPPbYYwwdGpsrdvv27fz5z38G\noFOnTjzyyCMAvPbaayxatAhJkrj11ls599xzcbvdTJ06Fbfbjdls5plnniE5uX7G18/OzubGG2+s\nl2MJ8RPRNPKqvHzqquBTVyWH/EF0Egx12HmwXTYXptl/9Sx19SkSDvOfZx+ncNcOxvzxHlr37J2w\nWNxfHiK4r5KUcR3RpTXdzgzCrxfXZHHw4EHeeOMN+vTpc0z53/72N6ZNm0bPnj2ZOnUqX331Fbm5\nuSxcuJB3330Xj8fDxIkTOfvss5kzZw5nnnkm119/PXPnzuWf//wnd911VzyfhtAAuMMRlpe7+by0\nis9LqigJhVEliXNSbPypTQYj0pJwqA3vxDkaifDZzGfZ9923DL/xVjoOGJywWAIHq6j64gCmXumY\n+4iL74SfF9dPU3p6Oi+++CL3339/bVkwGKSgoICePXsCMHToUFatWoXL5WLIkCHo9XocDgctWrRg\n9+7drFq1iscee6z2bydPnhzPpyAkSETT2Ozx8XWZm2VlbtZWeghrYNfJDHPYGZGWxFCHjaQGmCCO\nikYiLHzhaXasWs45V11Lz2GJG2Mt6g1R9s52lCQDKeLiO+EkxPWT9VN99svLy7HbfxjRMjU1FZfL\nRXJyMg6Ho7bc4XDgcrkoKSmpLU9NTaW4uPj0By7EXVTT2FHt55sKD6sqPKws91Aejk3E1dViZHJL\nJ+c77PRPspy2UV/rUyQcZuGM6exc8w3nXnUt/S76XcJi0aIaZe/tJFIVJP2mnsjGhptghYbjtL1L\n3n//fd5///1jym677TaGDBnys/c7US+jnyo/1R5JQsPljUT53u1lXWU1ayurWV9ZXZscWhhURqQl\ncY7DxtnJVpyGxjWwXSgYYOGM6exet5rzrr6evr8Zm9B43F8dwr+9jOSL22FoJYYeF07OaUsW48aN\nY9y4cXX+ncPhoKKiona/qKgIp9OJ0+lk3759P1nucrmw2Wy1ZULjEopq7Kj28b3bx3duLxuqvGyt\n9hGpyf3tzQZGpCUxMNnKwGQLrUyGxAb8K/g8buY/9VcO79zG+b+/iTNGXpTQePy7yqlacgBT73Qs\nZ4lJzISTl/DzT1VVyc3NZf369fTr148lS5YwadIk2rRpwxtvvMFtt91GeXk5xcXFtG/fnsGDB7No\n0SKmTJnCkiVL6jxTERJH0zRcwTDbq/1sr/ax1eNnq8fHDq+fQDSWGWyKTG+7mdtaZdDHbqav3UJq\ngq9/qC9VJcV88NjDVBYVMub2u+k0MLHv1XCZn7J3t6Nzmkn5XQfRTiGckrh+Kr/88ktef/119u7d\ny5YtW3jzzTeZNWsW06ZN46GHHiIajdKrVy8GDYpN9nL55Zdz1VVXIUkSf/7zn5FlmUmTJnHXXXcx\nceJE7HY706dPj+dTEH5CKKpxyB9kry/A7mo/u70Bdnn97PL6KQtFav8uTdXRzWri2hZp9LKZ6Wkz\n08akP+3DfidC4e4dfPz03wgHAlx6/19p2bVHQuOJBsKU/msLWgRSr+qCrG84XYmFxkHSmljFf35+\nPsOGDWPp0qXk5IirUeuDpmlUhSMc8gc56A9y0BfkgD/Ifl+A/b4Ah/xBwj96FzlUhY5mI+3NRjpb\njXSqWafrG1dbwy+1bcWXLH7leawpDsbe/RBpLVsnNB4tqlH65lb8O8pI+313jB1SEhqP0DDV9d3Z\nNM73hV/FG4lSGAhSGAhR4A9x+EfbBYEg+f4gnsix41bZFJm2JgPdrWYuSk+mndlIO7OBtiZDk6lG\nOlXRaISV7/2bNR+9R06X7lz0p/sw2xM/XWzVkv34t5WR/Nt2IlEIv1jz/FQ3A5qm4Y5EcQVDFAfD\nFAdDFAfCFAVDFNVsHwmGOBIIURmOHHd/h6rQwqCntUnP4GQrOUY9OUY9rUx6Whn1JOsUUef9I96q\nSha+8DQHvt9Aj/MvZNh1N6PoEn8m5VlbiPvLfCwDMrEMFA3awi8nkkUjEoxGKQtFKAmGKAmFKQmG\nKf3R2hWMbbtCIUqCYfzR42sYVUnCqdfh1KvkmgwMSraSqVfJMqpkG1SyDXqyDComMUT1SSvcvYP/\nPPsE3qoKht94Gz3Ov7BBJFLftlIqPtqNsVMKyb9t1yBiEhovkSwSIBCNUhWOUBmOUBmqWYcjVIQj\nVITClIcilIfDlAVr1qEwpcEw7shPD2GukyBV1ZGuV0lTdbQzG0ivSQhH106DjnRVJUVVmmSDciJo\n0SjfLvyY5W/PwepIZcJfppOR2z7RYQGxoTzK3t6Omm3FMbGLmJ9C+NVEsvgZmqYR0jR8kSi+6NF1\nFG/kR0s0SnUkgjcSpToSxROO4qnZd0cieMI/rKvCEdyRSG230ROxKDLJOoVUVYdD1dHaqCdVryNV\njS1pR7f1OtJUHUmiSijuqivKWfTS39m/MY/2/c/iwsm3Y7LaEh0WAKFiL6VztiDb9KT9Xzdkg+j5\nJPx6Iln8SF5lNbdsO0BVOIo/Glsip9hXzChLWBQFiyJj08nYFIV0VaWdScamU7AqCkk6BbuqYFdk\n7DqF5Jov/GSdQrKqoBfDmTdou9at4vNXXyTk83HB9VPoecGoBpOsw6U+XK9tAkki7druKDZ9okMS\nmgiRLH4kVa9jSErs16FJljEqMiZZwqTIGGUZsyJjkmVMioxFie2ba7YtioJZltE1gnGKhF/G53Gz\n7I1/sG3Fl6S3yWX0rVMT3i32x8KVAVyvb4ZwlPQbe6KKIceFeiSSxY+0Nhl4qlPLRIchNDCaprF7\n7SqWvvEKvqpKBl42gQGXXIHSgKatjbiDlLy2iWh1iPQbeqBmiqlRhfrVcN7tgtAAVZUUs3TWK+z9\ndi3prdtyyd0PNZhG7KMiVQFc/9xEpDJA2u+7o89pGG0nQtMikoUg/IRwKETewo9Z/cG7aGice9W1\n9Bl9MXIDmnEPYvNnl/zzeyLuEGnXdsfQJvEXAQpNk0gWgvA/9uatY9mcV6k4Uki7fgMYes2NJDkz\nEh3WccLlflz/jFU9pV3fXQw3LpxWIlkIQo3i/Xv5+t9vcOD7DaRk5/C7+x6hbe++iQ7rJ4Ur/Lhe\n/Z6oL0z69T3QtxRVT8LpJZKF0OxVFhex8r232LriS4wWK+ddfT29R/ymQQzX8VPCFQFcr276IVGI\nNgohDkSyEJqtqhIXaz6ay+ZlnyPLCv1/eylnXnwZRos10aGdULgyEDujqA6JRCHElUgWQrNTWXyE\ndZ98yOZlS9A06DFsJAMuGYfNkZbo0H5WpCpASU0bhah6EuJNJAuh2Sj5//buPDiK887/+HvuQ5rR\naEYaAeKyOIQ5ROJgbA4ZY7Cd7Hpj11YEhMA6TkyWoiDJDxWOIRWQKyUooEjWOUhS4LJlbBCWwT97\nf2FlErymKlsqOxhsLnMLCQmkGUkjaTSas2d+fwwMKMAKMJrR8X1VTXX3Mz3iaXrUH/XT3c9Te5FP\nP9jDqf85iFqtZsKsuTzyr/OwZvX+oXkVbyh+e2x7iKwfTpSgEEmX1LCIRCL8/Oc/p7a2FkVReOml\nl5gyZQqnTp2ipKQEgPz8fF555RUAtm/fTmVlJSqViuXLlzNr1iy8Xi/FxcV4vV7MZjNbtmzBZrMl\nczNEHxKLxaj54jCH/vx/qTl6BJ3ByEP/9CxT/vk50u2OVFfvjigdIdzbj6G0xp+jMIyQu55E8iU1\nLN5//31MJhO7du3i7NmzrF69mnfffZfS0lLWrFlDQUEBxcXFHDx4kLy8PPbt20d5eTkdHR0sXLiQ\nmTNnUlZWxtSpU3nxPVzwbwAAFhBJREFUxRfZvXs327ZtY9WqVcncDNEHBDt9nDj4EV/s/zMtl+tI\ny7Qzc8G/UfDkt3pNh393QmmPB0WkJUDW9ydgyJPnKERqJDUsvv3tb/PMM88AYLfbaW1tJRQKUV9f\nT0FBAQCzZ8+mqqoKt9tNYWEher0eu91Obm4u586do6qqivXr1yfWXbp0aTI3QfRisViMhvNnOPbR\nfk797SDhYIBBo8fyzWX/h3EzHuu1dzfdTqQ1SNP2YyjtQbJemIBxlJxBi9RJaljodNd/WcvKynjm\nmWfweDxYrddPqx0OB263G5vNht1uT5Tb7XbcbjdNTU2JcofDgcvlSt4GiF7J1+rh1P8c5Ph//4Wm\nSzVoDQbypxXytaf+mUGjxqS6evck0uyPP3Dnj5D1w0nS9CRSrsfCoqKigoqKii5lK1asoLCwkLff\nfpsTJ07wxz/+kZaWli7rxGK37hP8VuW3W1f0fyF/J+cPfcLJv31MzRdHiMWiDBo1hieXLCd/+mMY\nzOZUV/GehRt88d5jlSjZS+T2WNE79FhYFBUVUVRUdFN5RUUFH330EVu3bkWn0yWao65pbGzE6XTi\ndDqprq6+Zbnb7cZisSTKxMAQ7Oyk+sjfOV31N6o/P4QSDmPJymbqc9/hwZmzcQzt+z0GB2vbaXr9\nBCqtmux/L0CXI73Hit4hqc1Qly5dory8nLfeeguDwQDEm6by8vI4dOgQU6ZMYf/+/SxevJiRI0fy\n+uuvs2LFCjweDy6Xi9GjRzNjxgwqKytZtmwZ+/fvp7CwMJmbIJLM29JE9eFDnPt7FbXHv0CJREjL\ntFMw95vkP1rIkLHjUPWTwaICZz007ziJOl1P9ouT0NqNqa6SEAlJDYuKigpaW1v50Y9+lCh77bXX\nWLNmDWvXriUajTJ58mSmT58OwLx581i0aBEqlYqSkhLUajWLFy9m1apVLFy4EKvVyubNm5O5CaKH\nKZEwV86c5uLRw1w4cgj3xQsAZOQM4mvf/BfGPDyNwWPzUat7V++vX5Xvs0Y8e86ic5rI+sEkNFYZ\n4U70LqpYP2v4r6urY86cORw4cIChQ4emujqiG7FolKZLNdQeP0rtiS+4dOIY4YAflVpNbv54Hvj6\nFPIeehjH0OG9ZujS+ykWi9H+11q8B2oxjLbhWPQgaqM8KyuSr7tjp3wrRVIpkQiui+ep//IEdadO\nUn/6JAFvOwC2nMGML3ycEZMfYviEAgzm/t1eHwsrePacpfNzN+Zv5JD5r6NRafpHk5rofyQsRI/q\nbG/jytnTXDl7msunT3Ll/BkiwSAQD4dRD01l2IRJDJtQgDUrO8W1TZ5Ia5DmHScJ13dgfWoEltnD\n+uWZk+g/JCzEfePv8OKqPk/jhXNXX2dpczUCoFKrcY7MY9ITT5GbP57c/PF9pruN+y14sY3mt74k\nFo7i+LfxmMYPzP8H0bdIWIi7pkQieK7U03SphqbaGty11bgvVuNtdifWsWbnkJM3islP/hODR+eT\nkzcanVHu7un45AqtH5xHazPgWDJJbo0VfYaEhbitgK8Dz5V6PJfrablcT8vlSzTXXaK14TJRRQHi\nZwyZg3PJHTce58g8skfmkfPAKEwWeeL4RrFIlNb/PI/vkwYMYzNxLMhHbe5b3Y+IgU3CYgBTIhE6\nWppod7toczXS5mqgzdVIa8MVPI1XEheeIR4KtpzBOIYOY/TDj+IYOpysYSOwDxmKVi+3ef5vFG+I\n5re/JHSxHcusoVifHolKLdcnRN8iYdFPKZEwvlYPHS3NdLQ0421uxtvShLe5CW+zG29zE76WFmKx\naOIzKpUaS1YWtpzBjH1kOrZBQ8gcNITMIbnYcgb1uY74eoNQfQfNb54k2hnG/t18zJOlxwHRN0lY\n9BFKJEKw00ego4NARzt+rxe/tx1/exud7W10tnrwtbXia/Xga/Xgb2+76WdodDosjiwsjmyGTyjA\nmu3EmhV/ZeQMwuLIQqOVr8T90vmFC8+7Z1GbdWQvnYw+t/cO1ypEd+TI0INisRiRcIhwIEAkGCQU\n8BMOBAj5/YQCnfGp//o02NlJsNN3dd4XX/b5CPg6CAf8t/13tDo9powM0myZZDgHMWTsONIzHaRl\n2knPtJNud2BxZGFMt8jtmUkQi0Rp/fMFfFVX0I+04vjeg2gs0lQn+jYJixsokQgXv/iMkN+PEg4T\nCYdRwiEi4TCRUCg+HwoRCQWvTuPz4RuWw8EA4UCAcDBIJBjs0szzv1FrtOjNZgxmMwZTGnqzCVvO\nIAzmdAxpaRjT0jGkpWNMT8eUbsFosWBKt2LOyEBnNEkI9BKRlgDNO78kXNdB+sxcMr45EpVWHrQT\nfZ+ExQ3Of/YJ//mrDbd9X6vTo9Hr0OoNaPV6tDo9Wr0BncGAMd2CVqdHZzSi0xvQGgzoDEZ0BkO8\nzGhEbzSjMxrQG03oTear0/i8RqeTA34f13nUjWfvOYjFcCx6ENPErFRXSYj7RsLiBmOmTueFX/8R\nUKHV6dBcfWn1BjRarRzMxS1FgxFaP7hA52eN6IZZcMzPR5tlSnW1hLivJCxuoFKpsA+RzgfFnQte\naKXl3bMongCWJ4ZhnTNc+ncS/ZKEhRD3IBpUaKusxld1BY3DSPa/F2AYmZHqagnRYyQshLhL/lMt\ntL5/DqU1SPqMIVifHola37/G1xDiHyU1LJqbm/nZz35GMBgkHA6zevVqJk+ezKlTpygpKQEgPz+f\nV155BYDt27dTWVmJSqVi+fLlzJo1C6/XS3FxMV6vF7PZzJYtW7DZbMncDDFAKW1BWv/fBfzHmtBm\nm+RsQgwoSW1c/eCDD3j22WfZsWMHK1eu5NVXXwWgtLSUNWvWUF5eTkdHBwcPHuTSpUvs27ePnTt3\n8qc//YkNGzagKAplZWVMnTqVXbt28dRTT7Ft27ZkboIYgGKRKO0fX6Jhy2f4v2zB+vQIcn7ykASF\nGFCSembxwgsvJOavXLlCTk4OoVCI+vp6CgoKAJg9ezZVVVW43W4KCwvR6/XY7XZyc3M5d+4cVVVV\nrF+/PrHu0qVLk7kJYgCJxWIETjbTuq8apTmA8UE7tmfy0DrkTicx8CT9moXb7Wbp0qX4fD7Kysrw\neDxYrdd7KHU4HLjdbmw2G3a7PVFut9txu900NTUlyh0OBy6XK9mbIAaAYE07bf9VTehiO1qnmawf\nTsQ4JjPV1RIiZXosLCoqKqioqOhStmLFCgoLC9mzZw8HDx5k9erVbNjQ9SG42w0JfqvyfjZ8uOgF\nwg0+2vbXEDjZjDpdh+25UaQ9PBiVRp6xEQNbj4VFUVERRUVFXco+/fRT2trayMjIYNasWbz00kvY\n7XZaW1sT6zQ2NuJ0OnE6nVRXV9+y3O12Y7FYEmVCfFXhBh/tB2rxH2tCZdBgfWoE6TNz5S4nIa5K\n6gXu/fv389577wFw+vRpBg8ejE6nIy8vj0OHDiXWKSws5NFHH+Xjjz8mFArR2NiIy+Vi9OjRzJgx\ng8rKyi7rCnGvgrXtNL15ksb/OEzgjAfLE8MY/LOHsT4xXIJCiBsk9ZrFsmXLePnll/nLX/5CKBRK\n3C67Zs0a1q5dSzQaZfLkyUyfPh2AefPmsWjRIlQqFSUlJajVahYvXsyqVatYuHAhVquVzZs3J3MT\nRD8Qi8YInPHgPVhHqLoNlUmL5YlhWGbmyuh1QtyGKtbPGv7r6uqYM2cOBw4cYOhQ6bpDXBcLK/gO\nu+j4Wz0Rtx9Nhp70mUNJmzoItUHOIsTA1t2xU57gFv1epC2Ir+oyvk8biHZG0OWmY1+Qj2lSlvTj\nJMQdkrAQ/VIsGiN4oRXfJw34TzRBDIzjHVhmDEH/QIb0ICzEXZKwEP2K0h6i80gjvr83EmnyozZr\nSZ+RS/q0IWjtxlRXT4g+S8JC9HmxsIL/yxY6D7sInGmBKOhHWMmcMxzzxCxUOmlqEuKrkrAQfVJM\niRI830bn5y78J5qJBRXUVj2Wx4Zi/kYOumxzqqsoRL8iYSH6jFhYIXCuFf/xZvwnm4n5I6iMGkyT\nsjB/LRtDng2VWq5FCNETJCxEr6Z4QwROt+A/2ULwrIdYOBoPiAcdmCZlYRyTKc1MQiSBhIXoVWKR\nKMGadoLnWgmcbiF82QeAJkOP+Rs5mMY7MORloNJKQAiRTBIWIqViSpRQXQfB6jaCF9oIVbcRC0dB\nDfrhVqxPj8SYn4lucJrc7ipECklYiKSK+iOELnkJXmwjVNNOqNYbDwdA6zST9vAgDKNtGPIyUBvl\n6ylEbyG/jaLHxCJRwg0+QnUdhOq8hGrbibj88TdVoBuchnlKDoY8G4YHrGjS9amtsBDitiQsxH0R\n9UcIN/gIX/ERutxB+HIH4cZOUOJdj6nNWvTDrZgnO9EPt6AfbkFtkK+fEH2F/LaKuxLtDBN2+4m4\nOgm7OuPThk6UtmBiHXWaFt2QdNJn5qLPTUc/1IIm0yDXHITowyQsxE2i/giRlgCRZj+R5gCRJn98\n3u0n6gtfX1GrRpdtQv+AFd2gNHSD09APSkNt1UswCNHPSFgMINGQQtQbQvGGULzh6/PtIZT2YHza\nFiQWULp8Tm3RoXWYMD5oR+c0o802oc02o7Ub5SE4IQYICYs+KBaLEQtHifojRDsjRDvDN0998Zdy\ndRrtCCfuOupCDep0PRqrHq3DhCEvA22mEa3diMZuROswyrUFIURqwqKpqYlvfetb/O53v+ORRx7h\n1KlTiVHz8vPzeeWVVwDYvn07lZWVqFQqli9fzqxZs/B6vRQXF+P1ejGbzWzZsgWbzZaKzbgnsWiM\nWFghFlSIBm8xDUSuTwMKUX8kPu+PEL029Ucgcvsxq1Q6Neo0Xfxl1qLLNqNOjy9rLHo06bpEQKjT\ndHJ2IIToVkrCYtOmTQwbNiyxXFpaypo1aygoKKC4uJiDBw+Sl5fHvn37KC8vp6Ojg4ULFzJz5kzK\nysqYOnUqL774Irt372bbtm2sWrXqvtUt4gkQCyrEIlFiSiw+jUQhEiUWjs/Hwje8EsvK9eVQlFhI\nIRqOT+Ovq/O3+uv+VtQq1CYNKqMWtVGL2qRFl2FAbdSiMseX1SYtanM8ENQmbTwMzFpUOhn1TQhx\nfyU9LKqqqkhLS2Ps2LEAhEIh6uvrKSgoAGD27NlUVVXhdrspLCxEr9djt9vJzc3l3LlzVFVVsX79\n+sS6S5cuvW91859opnnHybv7kApUOg0qnTr+0qsTy2qTNn6xV6+Jlxs0qHQa1AYNKoMGtT4+VRlu\nKDNqURs1oFXLRWIhRK+R1LAIhUL8/ve/Z+vWrYkDvsfjwWq1JtZxOBy43W5sNht2uz1Rbrfbcbvd\nNDU1JcodDgcul+u+1c841oZ94TgAVFr11ZcqfuDWXg2Df5hHo5KDuhCi3+uxsKioqKCioqJL2WOP\nPUZRUVGXcPhHsdit2+JvVX67de+VSqfBXJB9X3+mEEL0Bz0WFkVFRRQVFXUpW7BgAdFolLfffpva\n2lqOHj3Kr371K1pbWxPrNDY24nQ6cTqdVFdX37Lc7XZjsVgSZUIIIXpWUvt5Li8v55133uGdd97h\n8ccfZ926dYwbN468vDwOHToEwP79+yksLOTRRx/l448/JhQK0djYiMvlYvTo0cyYMYPKysou6woh\nhOhZveIG+jVr1rB27Vqi0SiTJ09m+vTpAMybN49FixahUqkoKSlBrVazePFiVq1axcKFC7FarWze\nvDnFtRdCiP5PFbvfDf8pVldXx5w5czhw4ABDhw5NdXWEEKJP6O7YKcONCSGE6JaEhRBCiG5JWAgh\nhOhWr7jAfT8pSrzH1IaGhhTXRAgh+o5rx8xrx9B/1O/Cwu12A/C9730vxTURQoi+x+12M2LEiJvK\n+93dUIFAgOPHj5OdnY1GIx3qCSHEnVAUBbfbzcSJEzEajTe93+/CQgghxP0nF7iFEEJ0S8JCCCFE\nt/rdBW6RPEePHqW8vJxYLMby5cvJzc1NdZUGPJfLRWlpKTNnzrypI0+ROkeOHKGiogJFUVi8eDET\nJ05MdZXumpxZiJucOXOGuXPn8tZbbyXK1q9fz/z581mwYAFHjx4FYNeuXZSUlLBs2bKbuqMX99ed\n7hO1Ws38+fNTVc0B5073i8lkYt26dXz/+99PdJra10hYiC46Ozv55S9/ybRp0xJln376KTU1Neze\nvZvS0lJKS0sBiEQi6PV6srOzaW5uTlWV+7272SdZWVlyF2CS3M1+GTduHOFwmJ07d/Lcc8+lqspf\niYSF6EKv17Nt27Yu44RUVVUxd+5cAEaNGkVbWxsdHR2YTCaCwSANDQ0MHjw4VVXu9+5mn4jkuZv9\n4vV62bRpEytXrsRms6Wqyl+JXLMQXWi1WrTarl+LpqYmJkyYkFi+NsTt/PnzKSkpQVEUVq5cmeyq\nDhh3s0+OHTvGrl278Hq92Gw2nnzyyWRXd8C4m/3y3nvv4fP52Lp1K1OmTOHpp59OdnW/MgkLcdeu\nPZozYcIENmzYkOLaCLi+T6ZNm9alWUSk1rX90h/+mJJmKNEtp9NJU1NTYtnlcpGdLWOVp5Lsk96p\nP+8XCQvRrRkzZvDhhx8CcOLECZxOJ+np6Smu1cAm+6R36s/7RZqhRBfHjx9n48aN1NfXo9Vq+fDD\nD/ntb3/LhAkTWLBgASqVinXr1qW6mgOK7JPeaaDtF+kbSgghRLekGUoIIUS3JCyEEEJ0S8JCCCFE\ntyQshBBCdEvCQgghRLckLIQQQnRLwkKIu7R37142btzYY5/duHEje/fuvaefL0RPkbAQQgjRLXmC\nW4h7tGHDBo4ePUowGOS73/0uRUVFvPzyy9jtdk6cOEFLSwtLlixh7969eDyexAA5dXV1LFmyhIaG\nBp5//nm+853v8P7777N9+3ZycnIwGo2MGTOGjo4OiouL6ezsJBAI8Itf/IKCgoIUb7UYqOTMQoh7\nlJuby65du9i5cyevvvpqolyr1VJWVsbYsWM5cuQIb7zxBmPHjuWTTz4B4OLFi2zdupU333yT3/zm\nN8RiMX7961/zxhtv8Ic//IGamhoA3G43RUVF7Nixg5UrV7Jt27aUbKcQIGcWQtyztrY2FixYgE6n\nw+PxJMqv/fXvdDrJy8sD4iPYeb1eAB566CF0Oh2ZmZmkp6fj8XhIS0vD4XAk3r/2ma1bt/Laa68R\nCoUwm83J3DwhupCwEOIeHD9+nGg0yo4dO9DpdHz9619PvHfjsKY3zl/rhk2lUnX5WdFoFLVafdN6\nZWVl5OTksHnzZo4dO8amTZt6ZFuEuBPSDCXEPaivr2fQoEHodDoOHDiAoiiEQqE7+uznn3+Ooii0\ntLTg9/vJzMzE6/XS3t5OOBzm8OHDAHg8HoYPHw7AX//6V8LhcI9tjxDdkbAQ4h7MnTuXmpoaFi1a\nxKVLl3j88ccpKSm5o8/m5eXxk5/8hOeff56f/vSnaDQali9fzqJFi/jxj3/MmDFjAHj22Wd5/fXX\n+cEPfkBBQQFut5s9e/b04FYJcXvSRbkQQohuyZmFEEKIbklYCCGE6JaEhRBCiG5JWAghhOiWhIUQ\nQohuSVgIIYToloSFEEKIbklYCCGE6Nb/BxJWRlv9eTufAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWIN11fBeJLH",
        "colab_type": "code",
        "outputId": "3a66e2cf-40da-48fe-ef52-ed40fc3911f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "from sklearn.linear_model import LassoCV\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn import metrics\n",
        "\n",
        "reg = LassoCV( alphas = None, cv = 10, max_iter = 100000, normalize = True)\n",
        "reg.fit(X_train, y_train)\n",
        "print(\"Best alpha using built-in LassoCV: %f\" % reg.alpha_)\n",
        "print(\"Best score using built-in LassoCV: %f\" %reg.score(X_train,y_train))\n",
        "coef = pd.Series(reg.coef_, index = X.columns)\n",
        "print('Root Mean Squared Error:', round(np.sqrt(metrics.mean_squared_error(y_test, reg.predict(X_test))),2))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best alpha using built-in LassoCV: 0.011549\n",
            "Best score using built-in LassoCV: 0.530637\n",
            "Root Mean Squared Error: 7578.82\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BRpQT9qJKUML",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = reg.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UtNQ-tubHMlK",
        "colab_type": "code",
        "outputId": "bc042dbc-9750-4120-d666-4333bc6c4c0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "print('Mean Absolute Error:', round(metrics.mean_absolute_error(y_test, y_pred),2))\n",
        "print('Mean Squared Error:', round(metrics.mean_squared_error(y_test, y_pred),2))\n",
        "print('Root Mean Squared Error:', round(np.sqrt(metrics.mean_squared_error(y_test, y_pred)),2))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 5406.47\n",
            "Mean Squared Error: 57438444.92\n",
            "Root Mean Squared Error: 7578.82\n",
            "Accuracy: 52.63 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSxyEJJtfEHQ",
        "colab_type": "code",
        "outputId": "dd305f41-707b-410b-af0b-1c3f5eacc745",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "reg.alpha_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.011549432679875158"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgE3i4rieWDp",
        "colab_type": "code",
        "outputId": "f4573835-4d5b-4350-8ef4-09991a17c5d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "coef"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year            24392.050128\n",
              "manufacturer     -663.263661\n",
              "make               17.866474\n",
              "condition        1189.492702\n",
              "cylinders       12001.377367\n",
              "fuel           -16443.684403\n",
              "odometer       -20622.414784\n",
              "title_status    -5463.502233\n",
              "transmission     1207.495835\n",
              "drive           -3980.623690\n",
              "size              -25.708781\n",
              "type              384.276198\n",
              "paint_color       394.627085\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9iffFZeKeXqD",
        "colab_type": "code",
        "outputId": "dfb65823-e589-4a37-e0ea-2d5a042c01af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "imp_coef = coef.sort_values()\n",
        "import matplotlib\n",
        "matplotlib.rcParams['figure.figsize'] = (6.0, 7.0)\n",
        "imp_coef.plot(kind = \"barh\")\n",
        "plt.title(\"Feature importance using Lasso Model\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Feature importance using Lasso Model')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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arXfcp4iI/DX5ejXa7t27ycrK4uzZs5w8eRIPDw88PT05ceIECQkJZGRk3LEP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\n",
            "text/plain": [
              "<Figure size 432x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmikzjI-ecEr",
        "colab_type": "code",
        "outputId": "4612eaac-9418-490f-ff77-3e82ff828e1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "lasso001 = Lasso(alpha=100, max_iter=10e5)\n",
        "lasso001.fit(X_train,y_train)\n",
        "train_score001=lasso001.score(X_train,y_train)\n",
        "test_score001=lasso001.score(X_test,y_test)\n",
        "coeff_used001 = np.sum(lasso001.coef_!=0)\n",
        "print (\"training score for alpha=100:\", train_score001)\n",
        "print (\"test score for alpha =100: \", test_score001)\n",
        "print (\"number of features used: for alpha =100: \", coeff_used001)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training score for alpha=100: 0.5155703090056228\n",
            "test score for alpha =100:  0.5112134546357787\n",
            "number of features used: for alpha =100:  7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7aONYdpgSTb",
        "colab_type": "text"
      },
      "source": [
        "# KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TCsVBmvZgTmG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.metrics import pairwise_distances\n",
        "from sklearn import neighbors\n",
        "from math import sqrt\n",
        "from sklearn.metrics import mean_squared_error "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mShndPqmgWjW",
        "colab_type": "code",
        "outputId": "58838641-8d91-464b-bef4-607498797d10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "rmse_val2 = [] #to store rmse values for different k\n",
        "for K in range(15):\n",
        "    K += 1\n",
        "    model = neighbors.KNeighborsRegressor(n_neighbors = K)\n",
        "\n",
        "    model.fit(X_train, y_train)  #fit the model\n",
        "    pred=model.predict(X_test) #make prediction on test set\n",
        "    error = sqrt(mean_squared_error(y_test, pred)) #calculate rmse\n",
        "    rmse_val2.append(error) #store rmse values\n",
        "    print('RMSE value for k= ' , K , 'is:', error)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE value for k=  1 is: 6672.487716367083\n",
            "RMSE value for k=  2 is: 6102.4467442045325\n",
            "RMSE value for k=  3 is: 5925.696313689232\n",
            "RMSE value for k=  4 is: 5860.492295447539\n",
            "RMSE value for k=  5 is: 5826.966000576155\n",
            "RMSE value for k=  6 is: 5813.454050019631\n",
            "RMSE value for k=  7 is: 5812.716342211235\n",
            "RMSE value for k=  8 is: 5821.9579631759525\n",
            "RMSE value for k=  9 is: 5831.680669411934\n",
            "RMSE value for k=  10 is: 5840.569037743359\n",
            "RMSE value for k=  11 is: 5847.09913298263\n",
            "RMSE value for k=  12 is: 5859.284263911893\n",
            "RMSE value for k=  13 is: 5869.252038667072\n",
            "RMSE value for k=  14 is: 5877.502891046304\n",
            "RMSE value for k=  15 is: 5888.909782297415\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tD95BBqNgeW6",
        "colab_type": "code",
        "outputId": "13c430f2-54c4-4dfe-8b3c-bd78d0720402",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        }
      },
      "source": [
        "#plotting the rmse values against k values\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set(font_scale=1.3)\n",
        "\n",
        "curve = pd.DataFrame(rmse_val2) #elbow curve \n",
        "curve.plot(figsize=(8,5))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa0798dba90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vxXvvvefzmvXr16O2thbZ2dmwWCze9zl69CiysrK84woLC5GYmAilUukdc/DgQZ/3Kiws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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-muHK2xzgmbF",
        "colab_type": "text"
      },
      "source": [
        "######This means that we can make the best prediction by looking at two to six nearest neighbors of a data point. The number depends on the insight of the researcher."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EcZjphW2hW15",
        "colab_type": "text"
      },
      "source": [
        "Generating Model for K=6"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4ErIswUgm_t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Import knearest neighbors Classifier model\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "#Create KNN Classifier\n",
        "knn = KNeighborsClassifier(n_neighbors=6)\n",
        "\n",
        "#Train the model using the training sets\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "#Predict the response for test dataset\n",
        "y_pred = knn.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "91RLvc4PiURd",
        "colab_type": "text"
      },
      "source": [
        "Model evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SeDhlKHiQz-",
        "colab_type": "code",
        "outputId": "da4e1051-0c06-472c-a011-73fdc5c27832",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn import metrics\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.10357414043454514\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98OIPC3AX47l",
        "colab_type": "code",
        "outputId": "7abc55a9-d0d2-4dd0-f04d-734eb49c702a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "print('Mean Absolute Error:', round(metrics.mean_absolute_error(y_test, y_pred),2))\n",
        "print('Mean Squared Error:', round(metrics.mean_squared_error(y_test, y_pred),2))\n",
        "print('Root Mean Squared Error:', round(np.sqrt(metrics.mean_squared_error(y_test, y_pred)),2))\n",
        "accuracy = knn.score(X_test,y_test)\n",
        "print(\"Accuracy:\", round(accuracy*100,2),'%')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 4788.68\n",
            "Mean Squared Error: 62500378.49\n",
            "Root Mean Squared Error: 7905.72\n",
            "Accuracy: 10.36 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvdscFOPjm17",
        "colab_type": "text"
      },
      "source": [
        "# XGBOOST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s4HT5dBXjopf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import xgboost as xgb\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import accuracy_score\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHct1uLjjr-O",
        "colab_type": "code",
        "outputId": "7bf74856-e3e7-4b1a-f51e-cec2c8f8eabc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "import xgboost as xgb\n",
        "\n",
        "xg_reg = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,\n",
        "                max_depth = 5, alpha = 10, n_estimators = 10)\n",
        "xg_reg.fit(X_train,y_train)\n",
        "\n",
        "preds = xg_reg.predict(X_test)\n",
        "rmse = np.sqrt(mean_squared_error(y_test, preds))\n",
        "print(\"RMSE: %f\" % (rmse))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[16:27:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "RMSE: 9698.924628\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nh0dXcdJjsil",
        "colab_type": "code",
        "outputId": "d673f6dc-11f5-459b-ea67-5e6399635ae7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "# fit model\n",
        "xg_reg = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,\n",
        "                max_depth = 5, alpha = 10, n_estimators = 10)\n",
        "\n",
        "xg_reg.fit(X_train,y_train)\n",
        "\n",
        "predictions = xg_reg.predict(X_test)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[16:27:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OdIhx5gyDXiv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import metrics\n",
        "from sklearn.metrics import mean_squared_error as MSE"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbNlWWCEfXKw",
        "colab_type": "code",
        "outputId": "8f1bb7d4-e22c-4862-f5d3-51078a9373de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "print('Mean Absolute Error:', round(metrics.mean_absolute_error(y_test, predictions),2))\n",
        "print('Mean Squared Error:', round(metrics.mean_squared_error(y_test, predictions),2))\n",
        "print('Root Mean Squared Error:', round(np.sqrt(metrics.mean_squared_error(y_test, predictions)),2))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 6257.07\n",
            "Mean Squared Error: 94069138.93\n",
            "Root Mean Squared Error: 9698.92\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2d1G5b_9kBKJ",
        "colab_type": "text"
      },
      "source": [
        "##K-fold Cross Validation using XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqbEmlDbj7Fm",
        "colab_type": "code",
        "outputId": "a03e1989-d6df-4e5c-9c59-90033018414a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "data_dmatrix = xgb.DMatrix(data=X_train,label=y_train)\n",
        "params = {\"objective\":\"reg:linear\",'colsample_bytree': 0.3,'learning_rate': 0.1,\n",
        "                'max_depth': 5, 'alpha': 10}\n",
        "\n",
        "cv_results = xgb.cv(dtrain=data_dmatrix, params=params, nfold=3,\n",
        "                    num_boost_round=50,early_stopping_rounds=10,metrics=\"rmse\", as_pandas=True, seed=123)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[16:27:23] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:27:23] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:27:24] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwUkBJ1FkF35",
        "colab_type": "code",
        "outputId": "4699b997-dd1b-417a-95c3-f5699a2d7ad9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "A, b = X.iloc[:,:-1],X.iloc[:,-1]\n",
        "data_dmatrix = xgb.DMatrix(data=A,label=b)\n",
        "\n",
        "params = {\"objective\":\"reg:linear\",'colsample_bytree': 0.3,'learning_rate': 0.1,\n",
        "                'max_depth': 3, 'alpha': 10}\n",
        "\n",
        "cv_results = xgb.cv(dtrain=data_dmatrix, params=params, nfold=3,\n",
        "                    num_boost_round=50,early_stopping_rounds=10,metrics=\"rmse\", as_pandas=True, seed=123)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:588: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  data.base is not None and isinstance(data, np.ndarray) \\\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[16:27:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:27:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:27:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4cO0zUXkZ3N",
        "colab_type": "code",
        "outputId": "127ac625-f013-4f15-b413-e27f85077d87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "cv_results.head()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>train-rmse-mean</th>\n",
              "      <th>train-rmse-std</th>\n",
              "      <th>test-rmse-mean</th>\n",
              "      <th>test-rmse-std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.138402</td>\n",
              "      <td>0.006999</td>\n",
              "      <td>6.138801</td>\n",
              "      <td>0.015581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.802416</td>\n",
              "      <td>0.005503</td>\n",
              "      <td>5.804820</td>\n",
              "      <td>0.015229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.516693</td>\n",
              "      <td>0.003897</td>\n",
              "      <td>5.516724</td>\n",
              "      <td>0.016236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.271581</td>\n",
              "      <td>0.003637</td>\n",
              "      <td>5.271280</td>\n",
              "      <td>0.014906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.063736</td>\n",
              "      <td>0.002896</td>\n",
              "      <td>5.063564</td>\n",
              "      <td>0.014911</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   train-rmse-mean  train-rmse-std  test-rmse-mean  test-rmse-std\n",
              "0         6.138402        0.006999        6.138801       0.015581\n",
              "1         5.802416        0.005503        5.804820       0.015229\n",
              "2         5.516693        0.003897        5.516724       0.016236\n",
              "3         5.271581        0.003637        5.271280       0.014906\n",
              "4         5.063736        0.002896        5.063564       0.014911"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NeZQklyvkaYC",
        "colab_type": "code",
        "outputId": "3e30b4fa-6712-4f5e-c6aa-7a94e5e0bbfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "print((cv_results[\"test-rmse-mean\"]).tail(1))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "49    4.039161\n",
            "Name: test-rmse-mean, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L57cuU6ikdRR",
        "colab_type": "code",
        "outputId": "7841b051-8dc8-48cb-efe7-b83dda4edf64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "xg_reg = xgb.train(params=params, dtrain=data_dmatrix, num_boost_round=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[11:10:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgRtMMOzkrAv",
        "colab_type": "code",
        "outputId": "cf16d326-d986-4384-de56-6ee2b03d1c3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "xgb.plot_tree(xg_reg,rankdir='LR', num_trees=0)\n",
        "plt.rcParams['figure.figsize'] = [10,20]\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jUVJSAmtra5VjjYyMYGpqivDw8BrXunz5cqX1Hq1VZsOGDfjhhx9w6tSpCuevy36JiYn4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C/noEBwNeXvoewk1NF9TU1NTA1NQU69evxz/+8Q+kpaXBy8sLNTU1sLe3R0REBHbs2CG1i4uL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\nRERERERE1EswqCEiIiIiIiIi6iX+H3P3iiJwF/IVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1nM93Afjk1Ke",
        "colab_type": "code",
        "outputId": "b357c7d4-c3e5-443e-aba8-96e1b540cb39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "xgb.plot_tree(xg_reg,num_trees=0)\n",
        "plt.rcParams['figure.figsize'] = [30,30]\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Xrk1qaipJSUn07ds3enxv3TF7y1vw+s7n7Pz6vj5Thw4dqFixIhUrVqRq1ar0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\nCIIgCIIgCEIR/wdFGZkSfPZW/QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 2160x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0OL3jnxUkz6U",
        "colab_type": "code",
        "outputId": "c319056f-897c-43b8-ba57-7761b3f0520d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        }
      },
      "source": [
        "import seaborn as sns\n",
        "xgb.plot_importance(xg_reg)\n",
        "sns.set(font_scale=1.5)\n",
        "plt.rcParams['figure.figsize'] = [8, 8]\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aIiIi8nTydTArWbIkhQsX5uzZs+mOnT17FkdHR8qWLUuhQoWIiorKsM/D3N3d\nSUlJ4fz585QvX97Yfv78eVJSUkxulz6NB6HJ1dWVxo0bP7avjY1Nhu1eXl6cPn2apk2bZklNj7Ns\nUrtsH0NE5EncTUrJ8TF3797BlSuXAYiNjSU5OZmVK5cCULZsOTp06Gzse/r0H3z//f07M7/+egyA\n8PCdHDv2MwAvvtiLokXz5iOJ8qt8Hczs7Ox47rnn2LNnD3/99Zfx1uJff/3Fnj17aN68OXZ2dvj5\n+bFnzx6uXr1q3Gd25swZvv/+e5PrNW/enA8//JDPPvuMKVOmGNsfPOC1efPmWVK3n58fRYsWZfHi\nxTRo0CDd88RiYmKMK2mFCxfm9u3098fbt2/Pt99+y5YtW+jWrZvJsYSEBOzt7bNslezGjXjS0gxZ\nci1rof0UuYvmlbvk1Xk9EBa2jZ9/jjRpW7o0BIC6deubBLNTp04Yjz2wY8d249/bt++kYJbH5Otg\nBvDGG29w8OBB+vTpQ+/evYH7j8uws7PjjTfeAGD06NHs37+f3r1706tXL1JTU1mzZg2VK1fm5MmT\nxmtVrVqVbt26sW7dOuLi4qhfvz6RkZGEhYXx4osvZtnnajo5OTF58mQmTJhA9+7d6dSpE8WLF+fS\npUt89dVXtG7dmrFjxwJQs2ZNQkNDmTVrFrVq1cLR0ZFWrVoRGBjIzp07mThxIgcOHKBevXokJydz\n+vRpdu3axebNmzPcVyciIk9n4cIlme7bqVMXOnXq8s8dJc/I98GsSpUqrFmzhuDgYEJC7v9XSf36\n9QkKCjK+K7Fq1aosW7aMWbNmMX/+fMqWLcvo0aOJjo42CWYA06dPx8PDg82bNxMeHk7p0qV5/fXX\nGTlyZJbWHRgYSJkyZViyZAlLliwhJSWFsmXL8swzz9C58//911bPnj357bff2LJlCytXrsTd3Z1W\nrVphZ2fHJ598wvLly9m+fTu7d++mSJEieHl5MXz4cJN3oIqIiEjOsDE82Ekukk10KzP30LxyF80r\nd8mr84K8OzdLPC5DH2IuIiIiYiUUzERERESshIKZiIiIiJVQMBMRERGxEgpmIiIiIlZCwUxERETE\nSiiYiYiIiFgJBTMRERERK6FgJiIiImIlFMxERERErISCmYiIiIiVUDATERERsRIKZiIiIiJWQsFM\nRERExEoomImIiIhYCQUzERERESuhYCYiIiJiJRTMRERERKyEgpmIiIiIlVAwExEREbESCmYiIiIi\nVkLBTERERMRKKJiJiIiIWAkFMxERERErYW/pAkRERDISE3ODZcsWc+jQAWJiblCyZCmef74lQ4aM\nwMnJydLliWQLBTMREbE6N07ElBQAACAASURBVG/GMHz4IK5fjyYg4AUqVKjE2bNn2Lp1E7/8Eskn\nnyynUKFCli5TJMspmOUiP//8M/v372fgwIE4OztbuhwRkWyzatUKrly5zJQp02nbtoOxvWbN2rz3\n3iQ2bFjDoEFDLVihSPawMRgMBksXIZmzcuVKZs2axb59+/Dw8LB0OSKSj9xNSuF2XGKOjOXq6kSn\nTv5cvHievXu/x8bGxngsLS2NNm2a4eLiwuefb8uRerKKq6sT0dG3LV1GtsirczN3Xra2NpQqVfSp\nxtSKmWS7IdMjuHYzZ36gi0j2CA0OICd/7SYn36NAgYImoQzA1taWggUL8tdfl4iNjaV48eI5WJVI\n9tO7MnOJBQsWMGvWLABat26Nr68vvr6+tGrVildeeSVd//j4eGrXrs3s2bMBOHz4ML6+vuzevZsP\nPviApk2bUq9ePUaPHk10dHS68yMjIxk8eDD169enbt26DBo0iOPHj2fvJEVE/r8KFSpy+3Ycf/xx\n0qT9jz9Ocvt2HABXr16xRGki2UorZrlE27ZtOX/+PNu3b2fixImUKFECgN9++41169Zx69YtihUr\nZuwfERFBUlISXbt2NbnOokWLsLe3Z8SIEVy9epVVq1Zx/vx5Nm3ahIODAwAHDx5k+PDh1KlTh9df\nfx2DwcDGjRvp168fmzZtonLlyjk3cRHJl156qQ/793/L5MkTGTNmHBUqVObs2TPMnx+Mvb09KSkp\n3L1719JlimQ5BbNcomrVqtSoUYPt27fTpk0b4x6zWrVq8dlnnxEeHs5LL71k7B8aGoqPjw9Vq1Y1\nuU58fDxhYWEUKVIEAB8fH95++23CwsLo1q0baWlpTJ06FT8/P0JCQoznvfjii3Ts2JFFixYxd+7c\nHJixiFgbV9ece0RFmzbP8+GHHzJjxgzGj38DADs7O1588UViYmLYs2cPHh6uOVpTVsht9Zojr84t\np+elYJbLVaxYkdq1axMaGmoMZteuXeOHH35g3Lhx6foHBgYaQxmAv78/M2fO5LvvvqNbt26cOHGC\nqKgoRo8eTUxMjMm5DRo04MiRI9k7IRGxWjm1ufvBhuuGDf3YtCmMP/88zZ07d/Dy8qZEiZIMGzYA\nOzs7ihQplas2nOfVDfKQd+emzf/yRAICApgxYwZXr16lTJky7Ny5E4PBgL+/f7q+3t7eJq/t7e1x\nd3fn0qVLAJw7dw6AoKCgDMeytdW2RBHJOXZ2dlSp4mt8fePGdU6dOkndug30HDPJkxTM8oBOnTrx\n73//m7CwMIYMGcL27dtp1KgR5cqVM/taD56eMnHiRHx8fLK6VBGRJ5aWlsZHH31AWloaAwe+bOly\nRLKFglku8ve3jT9QsmRJmjVrRmhoKC1btuS3335j+vTpGfaNiooyeZ2SksKlS5do2rQpAJ6engA4\nOzsb257WskntsuQ6ImI5d5NScnS8O3fuMHz4QJo1a4Gbmzvx8fHs3RvOyZO/M3z4q9Sv3zBH6xHJ\nKQpmuYijoyMAt2+nv98dGBjI66+/zrx58yhQoAAdOnRI1wdg69atDB061LjPLCwsjFu3bvH8888D\nUKNGDTw9PVm+fDkdO3akcOHCJufHxMRQsmRJs+q+cSOetLS89Rxj7afIXTSv3MfBwYFKlaqwd284\nN25cp2DBQlSrVp3g4AU0btzE0uWJZBsFs1ykRo0aAMydO5dOnTrh4OBAy5YtcXR0pGXLlhQrVozd\nu3fTvn37R37Ab9GiRenXrx+BgYHGx2X4+PjQpUsX4P5+jmnTpjF8+HC6dOlCYGAgpUuX5sqVKxw4\ncAAvLy/mzJmTY3MWkfzJwcGB996baekyRHKcglkuUr16dcaNG8fatWvZv38/aWlp7Nu3D0dHR+Mq\n2caNG9M9u+xho0aN4tdffyUkJITExERatGjB5MmTjc8wA2jSpAkbNmxg0aJFrF69mjt37lC6dGnq\n1atHr169cmKqIiIi+ZKCWS4zYsQIRowYkeExBwcHihUrZrwtmRF7e3vGjx/P+PHjHztOjRo1+Pjj\nj5+qVhERETGPnn2QRyQmJhIWFkbHjh0pUKCApcsRERGRJ6AVs1zuxo0bHDx4kF27dhEXF0f//v0t\nXZKIiIg8IQWzXO706dMEBQXh4uLCu+++q8+xFBERycUUzHK5xo0bc/LkySzrJyIiIpajPWYiIiIi\nVkLBTERERMRKKJiJiIiIWAkFMxEREREroWAmIiIiYiUUzERERESshIKZiIiIiJVQMBMRERGxEgpm\nIiIiIlZCwUxERETESiiYiYiIiFgJBTMRERERK6FgJiIiImIlFMxERERErISCmYiIiIiVUDATERER\nsRIKZiIiIiJWQsFMRERExEoomImIiIhYCXtLFyAiIk9n2bLFrFjx6SOP29nZ8e23h3OwIhF5Ugpm\n+cjhw4cZMGAAixYtok2bNpYuR0SySPPmrfDw8EzXfubMH6xbt5rnnnveAlWJyJNQMBMRyeUqV65C\n5cpV0rXPnj0DAH//gJwuSUSekPaYiYjkQYmJiezbF0Hp0mVo3LiJpcsRkUzSiplku1Klilq6hGzh\n6upk6RKyheb1dO4mpXA7LjFHxnqcr7/eS0JCAi++2As7OztLlyMimaRgZiUWLFjAwoULiYiI4KOP\nPuLbb7+lUKFCDBo0iOHDh3P27FmmTZtGZGQkxYsXZ+zYsQQE3L89ERsby+LFi/n++++5ePEiNjY2\n1K9fn6CgIKpWrfrYcRMTE3nllVf4/fffWbFiBdWrVwfgjz/+YN68eRw5coS7d+9StWpVxowZw3PP\nPWf23IZMj+DaTcv/ohLJCaHBAdy2dBFAWNg2bGxs6Ny5q6VLEREz6FamlXn99dexs7MjKCgIX19f\ngoOD2bBhA0OGDKFKlSqMHz8eJycnJk6cyMWLFwG4cOECe/fupUWLFkyYMIEhQ4Zw6tQp+vXrx9Wr\nVx85VkJCAsOGDePUqVOsWrXKGMpOnjxJr169iIqKYvjw4YwfPx6AYcOGcejQoez/IojIUzl//hzH\njv1M/fqNcHNzt3Q5ImIGrZhZmfr16zNlyhQAunfvTrNmzZg6dSrTpk2jR48eADRp0oSOHTuybds2\nRo0aha+vL+Hh4dja/l/ODggIoGPHjmzatIlRo0alGyc+Pp6hQ4dy8eJFVq9eTaVKlYzHZs6cibe3\nNxs3bsTBwQGA3r17061bN+bOnUuTJtqvImLNwsK2AdClizb9i+Q2CmZW5kH4AihYsCC+vr5ERkYS\nGBhobK9YsSLOzs7GFbMCBQoYj6WmphIXF4ejoyMVKlTgf//7X7ox4uLiGDx4MFevXmX16tVUqFDB\neCw2NpbDhw8zbtw4bt82vSHj5+fHypUrSUxMpHDhwlk2Z5G8Jif36f19rJSUFCIidlG8eHG6d+9q\n8vMhN9Fex9wnr84tp+elYGZlypUrZ/LayckJFxcX48rVw+1xcXEApKWlsWrVKtatW8fFixdJTU01\n9itevHi6MaZNm0ZycjKhoaEmoQzg/PnzGAwGgoODCQ4OzrDG2NhYBTORx4iOzpldZq6uTunG+vbb\nr7l+/To9evTm1q0kIClHaslKGc0rL8ir84K8Ozdz52Vra/PUb3hTMLMyGb176lHvqDIYDACEhIQw\nb948unfvzpgxYyhWrBi2trbMnDnT2Odhbdq0ISwsjKVLlzJjxgyTY2lpacD9/WRNmzbNcNySJUua\nNScRyTk7dty/jalnl4nkTgpmeUB4eDiNGzdm5syZJu1xcXGUKFEiXf/27dvzzDPPMGnSJJycnJgw\nYYLxmKfn/aeHFyxY8JHBTESs0/Xr0Rw+fIhq1WpQqVJlS5cjIk9AwSwPsLOzS7cytmvXLq5evYq3\nt3eG5/To0YOEhARmzZqFk5OT8Q0CpUqVolGjRqxfv56+ffumWx2LiYkxe8Vs2aR2ZvUXyc3uJqVY\nbOydO0NJTU2lS5fAf+4sIlZJwSwPaNGiBYsWLWLixInUq1ePU6dOERoaalz9epRBgwYRFxfH/Pnz\nKVasGP369QPg3XffpW/fvvj7+9OjRw88PDy4du0aP/30E0lJSaxdu9as+m7ciCctLf0t1dxM+yly\nl7w6r78bMOBlBgx42dJliMhTUDDLA0aOHEliYiKhoaHs3LmT6tWrs3jx4kdu3n/Y66+/zu3bt5k+\nfTpFixYlMDAQHx8fNm3axIIFC/jiiy+Ii4vDxcWFGjVqMGDAgByYkYiISP5kY8hod7hIFtKKWe6h\neeUumlfuklfnBXl3bpZ4V6ae/C8iIiJiJRTMRERERKyEgpmIiIiIlVAwExEREbESCmYiIiIiVkLB\nTERERMRKKJiJiIiIWAkFMxEREREroWAmIiIiYiUUzERERESshIKZiIiIiJVQMBMRERGxEgpmIiIi\nIlZCwUxERETESiiYiYiIiFgJBTMRERERK2F2MPv111/58ssvTdq+/vprAgMDadmyJfPmzcuy4kRE\nRETyE7OD2cKFCwkPDze+vnLlCmPHjuWvv/6iQIEChISEsGXLliwtUkRERCQ/MDuY/f777zRs2ND4\nOiwsjNTUVLZt20Z4eDhNmzZl48aNWVqkiIiISH5gdjC7efMmLi4uxtfff/89jRo1oly5cgC0bt2a\ns2fPZl2FIiIiIvmE2cHM2dmZ69evA3Dv3j1+/vlnkxU0W1tbkpKSsq5CERERkXzC3twTqlatypdf\nfsnzzz9PREQESUlJ+Pn5GY9fvHiRkiVLZmmRIiIiIvmB2cHslVdeYciQIXTr1g2DwcCzzz5L7dq1\njce/+eYb6tSpk6VFioiIiOQHZgezhg0bsnnzZvbv30/RokXp2rWr8djNmzdp3Lgxbdu2zdIiRURE\nRPIDs4MZQKVKlahUqVK69hIlSjB58uSnLkpEJC+Ii7vFqlUr2L//G6Kjr+Ho6EiFCpUYOnQkderU\ns3R5ImKFniiYAVy+fJlDhw5x/fp1/P39cXNzIzk5mZiYGEqWLImDg0NW1ilPqFWrVjzzzDP8+9//\ntnQpIvnKlSuXGT16BImJd+jcOQBPTy8SEuI5c+Y00dHXLF2eiFipJwpmc+fOZdmyZaSkpGBjY0Ot\nWrVwc3Pj7t27dOjQgbFjxzJgwICsrlVEJNd4//3JpKamsnLlBpNHDImIPI7Zwezzzz9n8eLF9OnT\nhxYtWjB8+HDjMScnJ1q2bMlXX32lYGYldu/ejY2NjUVrKFWqqEXHzy6urk6WLiFb5MV53UtOzdHx\nfv45kmPHfuaNN4JwcXEhJSWFlJQUChUqlKN1iEjuY3YwW7t2La1bt+bdd9/l5s2b6Y5XrVqVNWvW\nZElx8vQKFChg6RIYMj2CazcTLV2G5GOhwQE5Ot6hQwcAKFOmLG+9NZbDhw+SmpqKh4cXgwcPpX37\nTjlaj4jkHmYHs7Nnz9KrV69HHi9RokSGgU2yR3x8PPPmzWPfvn1cu3YNJycnqlatSlBQEDVq1Ei3\nx6xVq1ZcunQpw2vt27cPDw8PAP744w/mzZvHkSNHuHv3LlWrVmXMmDE899xzOTY3kdzq/PkoAP7z\nnxl4enryr39NJTk5mQ0b1jBt2rukpKTQuXPXf7iKiORHZgezAgUKcPfu3Ucev3z5Mk5Oee9WiLWa\nMmUK33zzDf369cPT05OYmBh++uknTp8+TY0aNdL1f+edd0hISDBpW7hwIVevXsXR0RGAkydP0qdP\nH9zc3Bg+fDgFCxYkNDSUYcOGsWzZMpo0aZIjcxPJre7cuQOAo6Mj8+cvNr4ZqlmzFrz0UgBLliyi\nY0d/bG3N/vAVEcnjzA5mtWvXZu/evQwePDjdsXv37rF9+3bq16+fJcXJP/v222955ZVXGDp0aKb6\nt2nTxuT1Z599xvnz55k5c6bxExtmzpyJt7c3GzduNP5C6d27N926dWPu3LkKZpIr5eTeOWfnIgB0\n7doFN7f/+yQUV1cn2rRpzdatW4mPv57hY4fMlRf3BILmlRvl1bnl9LzMDmaDBw9m+PDhTJw4kRde\neAG4/2DZQ4cOMW/ePC5fvszs2bOzvFDJmLOzM0eOHKF79+6UKFHCrHOPHj3K7Nmz6dmzJ927dwcg\nNjaWw4cPM27cOG7fvm3S38/Pj5UrV5KYmEjhwoWzbA4iOSE6+vY/d8oixYrdD2OFCzulG7dIkWIA\nREVdxtm59FON4+qa/vp5geaV++TVuZk7L1tbm6d+w5vZwaxZs2ZMnjyZWbNmsXXrVgDefPPN+xez\nt2fq1KlaMctBQUFBTJgwAT8/P2rVqkXz5s3p2rUr7u7ujz3v6tWrjBkzhurVqzNp0iRj+/nz5zEY\nDAQHBxMcHJzhubGxsQpmIo9RrVoNtm79kmvX0j+v7MEzzEqU0GcKi0h6T/Qcsz59+tC6dWt27drF\nn3/+icFgwNvbm06dOuHm5pbVNcpjdOrUiYYNG7J3714OHDjAkiVLWLx4MQsWLKBZs2YZnpOcnMyY\nMWNIS0tj/vz5Ju/cTEtLA2DYsGE0bdo0w/P1IfUij9esWQvmzQsmImIXAwcOMe7fvH79Ovv3f4On\npxceHp4WrlJErJFZwezevXscP34cFxcXvLy8GDRoUDaVJeYoXbo0ffr0oU+fPsTExPDCCy/wySef\nPDKYzZgxg2PHjrFs2TLKlStncszT8/4vi4IFCz4ymJlr2aR2WXIdkSeV088xc3Z2ZtSoMcyZM5MR\nIwbRuXNXkpNT2Lp1E8nJyYwd+1aO1iMiuYdZwczGxob+/fvz9ttv6wGyViA1NZU7d+6YvAu2ZMmS\nlC1blqSkpAzP2bJlC+vXr+fNN9/McBN/qVKlaNSoEevXr6dv377pVscefOSWOW7ciCctzWDWOdZO\n+ylyF0tsSg4IeIHixYuzdu0qli4NwcbGlpo1azFlynRq166b4/WISO5gVjBzcHCgVKlSGAx565ds\nbpWQkEDz5s1p164dVatWpUiRIvzwww/897//ZcKECen6x8TEMGXKFMqVK0fp0qXZtm2byfG2bdvi\n6OjIu+++S9++ffH396dHjx54eHhw7do1fvrpJ5KSkli7dm1OTVEkV2vevBXNm7eydBkikouYvces\nffv2hIeHM2DAAIt/1E9+V6hQIXr37s2BAwfYs2cPBoMBLy8vpkyZQp8+fdL1v3PnDklJSVy+fJm3\n33473fF9+/bh6OiIj48PmzZtYsGCBXzxxRfExcXh4uJCjRo1tFIqIiKSjWwMZi5//fnnn4wbN46S\nJUsycOBAypcvn+Hnv5UpUybLipTcTbcycw/NK3fRvHKXvDovyLtzyxWPy+jUqRM2NjYYDAYOHTr0\nyH6///77UxUmIiIikt+YHcxGjBihW5giIiIi2cDsYDZ27NjsqENEREQk39Mn6IqIiIhYCbNXzCIj\nIzPVTx/LJCIiImIes4NZnz59MrXHTJv/RURERMxjdjCbNm1aurbU1FTOnz/Ptm3b8PT0pHv37llS\nnIiIiEh+YnYw69GjxyOPDRs2jBdeeMHkQ7FFREREJHOydPN/iRIl6NGjB59++mlWXlZEREQkX8jy\nd2UWK1aMqKiorL6siIiISJ6XpcHs3r17hIaG4urqmpWXFREREckXzN5jNnny5Azbb926RWRkJNev\nX+fNN9986sJERERE8huzg9kXX3yRYbuTkxPe3t4EBQURGBj41IWJiIiI5DdmB7PffvstXZuNjQ22\ntvoQAREREZGnYXaaun79OikpKdjZ2Rn/PBzK7t27x9WrV7O0SBEREZH8wOxg1qJFCyIiIh55fO/e\nvbRo0eJpahIRERHJl8wOZgaD4bHH09LSMvWRTSIiIiJi6ok2hj0ueJ09exYnJ6cnLkhEREQkv8rU\n5v+tW7eyfft24+vFixezefPmdP1iY2M5ceIEbdq0yboKRURERPKJTAWz2NhYzpw5A9xfLbt27Rpx\ncXEmfWxsbHB0dCQgIEDPMRMRERF5ApkKZoMGDWLQoEEAVK1alUmTJtGlS5fsrEtEREQk33mi55jZ\n2dllRy0iIiIi+ZrZm/8VykRERESyh9krZgAXLlxg1apVHDt2jFu3bmX4CI3w8PCnLk5EJDeLi7vF\nqlUr2L//G6Kjr+Ho6EiFCpUYOnQkderUs3R5ImKFzA5mf/zxB7179+bu3bt4e3tz7tw5KlasyM2b\nN7l58yYeHh6ULl06O2oVEck1rly5zOjRI0hMvEPnzgF4enqRkBDPmTOniY6+ZunyRMRKmR3M5s+f\nj52dHVu3bqVkyZI0bdqUyZMn06RJE9atW8eCBQuYNm1adtQqIpJrvP/+ZFJTU1m5cgMuLi6WLkdE\ncgmz95gdPXqUnj17Urly5XQPmu3Tpw/PPfccH3zwQZYVKCKS2/z8cyTHjv1Mnz79cXFxISUlhbt3\n71q6LBHJBcxeMYuPj8fLywsABwcHAO7cuWM83qBBAz766KMsKk+s0Z07d3B0dMx0/1KlimZjNZbj\n6po3P+EiL87rXnJqjo536NABAMqUKctbb43l8OGDpKam4uHhxeDBQ2nfvlOO1iMiuYfZwczFxYUb\nN24AULRoUQoXLkxUVJTx+O3bt0lJScm6CgWAL7/8knfeeYctW7ZQvXp1k2Nz585l+fLlfP/99xQr\nVozIyEgWLFjAL7/8QlpaGnXr1iUoKIiaNWsazzlx4gQrV67kxx9/5Nq1azg7O/P888/z1ltvUaJE\nCWO/BQsWsHDhQnbt2sX8+fPZv38/1atXZ/Xq1Zmufcj0CK7dTHz6L4LIEwoNDsjR8c6fv/8z8T//\nmYGnpyf/+tdUkpOT2bBhDdOmvUtKSgqdO3fN0ZpEJHcw+1amr68vx48fN75u2LAhq1evJjIykqNH\nj7Ju3Tp8fX2ztEiB9u3bU6hQIUJDQ03aDQYDoaGhNG/enGLFinHw4EEGDBjAvXv3eP311xkzZgxX\nrlyhX79+nD592njewYMHuXDhAi+88AKTJ0+mU6dO7Ny5k+HDh2f4LtvRo0eTlpZGUFAQXbvqF4rI\n4zy4i+Do6Mj8+Ytp164jnTt3ZdGipRQt6sSSJYtIS0uzcJUiYo3MXjHr3Lkza9eu5e7duxQqVIgx\nY8bQv39/+vbtC0DBggWZM2dOlhea3xUtWpTWrVuzc+dO3nrrLeP+vsjISC5dusSECRNIS0tj6tSp\n+Pn5ERISYjz3xRdfpGPHjixatIi5c+cC9/cDvvzyyyZj1K1bl3HjxvHTTz/RsGFDk2M1atRg9uzZ\n2TxLkbyhYMGCALRp09645QPA2dkZP7/n2b17B+fPR1G+fAVLlSgiVsrsYNalSxeTj2OqWbMmYWFh\nREREYGtrS4sWLfD29s7SIuW+gIAAduzYwZEjR2jcuDEAoaGhODs706JFC06cOEFUVBSjR48mJibG\n5NwGDRpw5MgR4+tChQoZ/56UlERCQgJ16tQB7n+6w9+DWa9evbJrWiI5Iif3znl5uXPwIHh7u6cb\n19PTDQA7u5QsqSkv7gkEzSs3yqtzy+l5PdEDZv/O3d2dwYMHZ8Wl5DH8/PxwcXEhLCyMxo0bk5yc\nzK5du2jfvj0FChTg3LlzAAQFBWV4vq3t/925jo2NZeHChezcudO4Z/CB27dvpzvXw8Mj6yYiYgHR\n0em/r7NLhQo+APz55/l040ZFXQTAxqbQU9fk6uqUo/PKKZpX7pNX52buvGxtbZ76DW9PHMzu3r3L\nL7/8wo0bN2jcuDGlSpV6qkLkn9nZ2eHv78+WLVuYPHkyBw4cIDY21rjn68HesIkTJ+Lj4/PYa73x\nxhv897//ZciQIVSrVg1HR0fS0tIYOnRohnvMHl5hE5HHa9asBfPmBRMRsYuBA4cY38V8/fp19u//\nBk9PLzw8PC1cpYhYoycKZhs3biQ4ONi4srJ8+XKaNGnCjRs3aN26NZMmTeLFF1/M0kLlvoCAAFau\nXMl3333Hzp07cXNzo1GjRgB4et7/Qe/s7EzTpk0feY1bt25x6NAhRo8ezWuvvWZsf7DiJiJPx9nZ\nmVGjxjBnzkxGjBhE585dSU5OYevWTSQnJzN27FuWLlFErJTZwWzPnj1MmTKFFi1a0LJlS6ZMmWI8\nVqpUKZo0acKePXsUzLJJ9erV8fHx4fPPP+fIkSP069fP+EaAGjVq4OnpyfLly+nYsSOFCxc2OTcm\nJoaSJUs+8oPoP/vss2ypedmkdtlyXZHMyunnmAEEBLxA8eLFWbt2FUuXhmBjY0vNmrWYMmU6tWvX\nzfF6RCR3MDuYLV26lEaNGhESEsLNmzdNghlArVq12LRpU5YVKOl17drV+OkKDz+6ws7OjmnTpjF8\n+HC6dOlCYGAgpUuX5sqVKxw4cAAvLy/mzJlD0aJFadSoEUuXLiU5OZkyZcpw4MABLl68mC313rgR\nT1pa+tujuZn2U+QultqU3Lx5K5o3b2WRsUUkdzL7OWYnT56kffv2jzzu6urK9evXn6ooebyuXbti\na2tL1apV0+0la9KkCRs2bMDHx4fVq1czbdo0tm3bhqenp8k7K4ODg/Hz82PdunV8+OGH2Nvb8+mn\nn+b0VEREROQhZq+Y2draPvbBiNHR0eluoUnWsre3x8bG5pEPeq1RowYff/zxY69RpkwZFi5cmK79\n5MmTJq9Hjx7N6NGjn7xYERERybQnevL/wYMHMzyWlpZGeHi4yUf/SNb78ssvAfD397dwJSIiIpKV\nzA5mffv25ZtvvmHhwoXEx8cb28+fP8/YsWM5deoU/fr1y9Ii5b5Dhw6xevVqFi9eTIcOHShTpoyl\nSxIREZEsZPatTH9/f06cOMHChQuNt8uGDRtGamoqBoOBV199lZYtW2Z5oQIff/wx//3vf6lfvz4T\nJkywdDkiIiKSxZ7oOWZBQUG0a9eO7du3c/bsWQwGA97e3gQEBFC3rt4Gnl1Wr15t6RJEREQkG2Uq\nmB07dgwvLy+KFy9ubKtduza1a9fOtsJERERE8ptM7THr2bMn+/fvN75OSEjgzTff5PTp09lWmIiI\niEh+k6lg9vfPTrx3P1c8dwAAIABJREFU7x47duwgOjo6W4oSERERyY/MflemiIiIiGQPBTMRERER\nK6FgJiIiImIlMv24jG+//db4GZiJiYnY2Niwe/duTpw4ka6vjY0NgwYNyrIiRURERPKDTAezsLAw\nwsLCTNo2btyYYV8FMxERERHzZSqYrVq1KrvrEBEREcn3MhXMnnnmmeyuQ0RERCTf0+Z/ERERESuh\nYCYiIiJiJRTMRERERKyEgpmIiIiIlVAwExEREbESCmYiIiIiVkLBTERERMRKKJiJiIiIWAkFMxER\nEREroWAmIiIiYiUy/SHmIiJ5gZ9fwwzbCxcuzJ49+3O4GhERUwpm2ax///4ArF69GoDDhw8zYMAA\nVq1aRePGjbNkjOy4pkheVqdOPbp27WbSZm+vH4ciYnn6SSQi+Y6bm/v/a+/ew2rK9z+Av7sIURGp\npouJ0U5pKrlFJIVKxIiS3E4zLuNuzEwGZ9zGGBPjCINcp3GXKAq5H7mNMThziGGEdBFJ9/v+/TG/\n9rGn0GXXWnt7v57nPM/pu9Ze38+n3eze1vquFfr39xK6DCKiChjM6lnnzp1x8+ZNNGjQQOhS6k2L\nFk2FLqFOGBjoCF1CnaivvgoKS5CdlV8vc1WmuLgYxcXF0NbWFqwGIqK/YzCrZ+rq6mjYsKHQZbxR\nfn4+GjdurLDjBS05jqcvhPsFTOIUvcIH2QLNfebMSRw/HovS0lI0a9Ycbm598cknn6JpU9X8RwQR\nKQ/elfkGKSkpmDNnDpydnWFra4u+fftiyZIliIiIgEQiwa1btyq85ocffoCtrS1evnxZ6TEvX74M\niUSCy5cvy8ZGjRoFHx8f3L17F6NGjYKdnR169uyJsLCwCq9PTU3Fp59+Cnt7ezg5OWHp0qUoKiqq\ndK5r165h3Lhx6NixI+zt7TF27Fj8/vvvcvsEBwejU6dOSExMRFBQEBwcHLBw4UIAQGJiIqZOnYoe\nPXrA1tYWvXr1wsyZM5GdLdSvU6Laa9/eBuPGjcfixd9h7twFcHTshIiIvZg8+WPk5eUJXR4RveN4\nxuw10tLSMGzYMOTm5sLPzw8WFhZITk5GTEwMZsyYgUWLFiE6OhrW1tay10ilUkRHR8PFxQV6enrV\nmi8zMxMff/wxPDw84OnpiaNHjyIkJASWlpZwcXEBABQUFGDMmDFISUnB6NGjYWBggEOHDuHSpUsV\njnfhwgWMHz8ednZ2mDZtGqRSKfbs2YPAwEDs378fH3zwgWzfkpISBAUFoVu3bggODoauri6KiooQ\nFBQEDQ0NjB07Fs2aNUNqaipOnz6NrKws6Oio5mU8Un1hYdvlvvb09Ebbtu2wceM67Nu3C2PGBAlU\nGRERg9lrhYSEICMjAxEREWjfvr1sfMaMGVBTU4ObmxtiYmLwxRdfQE1NDcBfZ6iePHmC4ODgas+X\nmpqKFStWwNvbGwDg6+uLPn36ICIiQhbM9uzZg8TERKxZswZ9+/YFAAwfPhw+Pj5yxyorK8OCBQvg\n7OyM9evXy8Z9fX3h6emJtWvX4ocffpCN5+fnY9CgQZg+fbps7Pbt20hKSsK+ffvw4YcfysanTp1a\n7d6IXqc+1+m9aa5p0z7F1q1huHr1EmbPnlFvNSkC1zoqF1XtC1Dd3uq7LwazSpSVleHkyZNwd3eX\nC2UAZCHMx8cHR44cwZUrV2SPqIiOjoauri569+5d7Tl1dHQwYMAA2ddaWlqwtbXF48ePZWPnzp2D\nsbEx3N3dZWONGzfG8OHD8f3338vGEhIS8PDhQ0ydOhUZGRly8zg6OuLKlSsV5vf395f7unytzenT\np2FlZQUtLa1q90T0Nunp9XNZ3MBA561ztWxpgPT05/VWkyJUpS9lxL6Uj6r2Vt2+1NXVan3DG4NZ\nJTIyMpCbm4t27dq9dh9nZ2e0bNkShw8fRteuXVFcXIzY2Fj079+/RiHG2NhYFvrK6enp4c6dO7Kv\nnzx5AnNz8wr7WVhYyH2dmJgIAJg9e3alc6mryy8t1NLSgqGhodyYmZkZxo0bh3Xr1mHbtm3o0qUL\nXF1d4e3tzQXSpHIKCwvx9GkabGxshS6FiN5xDGY1pKGhAW9vb0RGRmL+/PmIj49HZmYmBg0aVKPj\n/T0s1YZUKgUAzJkzB5aWlm/d/3V3iQYHB+Ojjz7CyZMncf78eSxcuBDr16/Hnj17KgQ5ImXw8mUm\n9PSaVRjftGk9SktL0aNHTwGqIiL6HwazSujr66NJkyb4448/3rifj48Ptm3bhnPnziEmJgbvvfce\nOnfuXGd1mZiY4P79+5BKpXJnzR48eCC3n5mZGQBAV1cX3bt3r9WclpaWsLS0xKRJk3Djxg0MHz4c\nu3btwowZVV+Hs3lev1rVQKqpoLCk3ufcvn0z/vvf39GxYycYGhoiLy8fly7F49q1q7C27gBfX796\nr4mI6FUMZpVQV1eHm5sbjhw5glu3blW487I8FFlbW8PS0hJ79+7FlStXEBgYWOEyoyL16tUL58+f\nx4kTJ2SL//Pz87F37165/WxsbGBmZoYtW7bA09OzwjPJMjIyoK+v/8a5cnJy0KhRI7k/U9OuXTto\namqisLCwWnU/f56DsjJptV4jdlxPoZwcHByRmPgAsbGHkZX1Eurq6jA1Ncf48Z/Cz2+k6J8xSESq\nj8HsNWbNmoX4+HiMHDkS/v7+sLCwQEpKCmJiYnDs2DHZfoMGDUJISIjs/9el4cOHY8eOHZg9ezZG\njx6Nli1b4tChQ2jUqJHcfhoaGli8eDHGjx+PgQMHYvDgwWjVqhVSU1MRHx8Pc3NzuZsFKnPp0iUs\nWrQI/fv3h4WFBcrKyhAVFQU1NTX079+/LtskqjM9e/ZGz569hS6DiOi1GMxew9jYGHv37sWqVasQ\nGRmJ3NxcGBsbV7jjctCgQVi5cqXskl9daty4MbZt24bFixcjPDwcjRo1wsCBA9GrVy98/PHHcvs6\nOTlh9+7dWLt2LcLDw5GXl4dWrVrBwcGhwh2YlZFIJHB2dsaZM2ewZ88eNG7cGBKJBGFhYbC3t6+r\nFomIiN5patLyleJUI8+fP0fPnj3x2WefISiID6asDC9lKg/2pVzYl3JR1b4A1e1NiMdl8E8y1VJE\nRAQAyB4MS0RERFRTvJRZQxcvXsS9e/ewYcMGeHh48PERREREVGsMZjW0bt06/Pbbb+jYsWON/gQT\nERER0d8xmNVQeHi40CUQERGRiuEaMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKR\nYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIi\nIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIlEpKCjA\nsGE+cHbuhJUrvxO6HCKiesVgpiJu3LiB4cOHw87ODhKJBElJSQqfQyKRIDQ0VOHHJXrVpk3rkZn5\nQugyiIgEwWCmAoqLizF9+nTk5eVh7ty5WL58OfT19YUui6ja7txJwL59uxAUNF7oUoiIBMFgpgIe\nPXqElJQUBAUFYfjw4fDx8YG2trbQZRFVS2lpKb77bgm6dnWCi0sfocshIhKEptAFUO1lZGQAAHR0\ndASupHItWjQVuoQ6YWAgzu93bRQVlwo29549O/HoUSK++Wa5YDUQEQmNwUzJBQcHIzIyEgAwefJk\nAECXLl1k28PDwyvsf+XKFZw6dUo2VlZWhq1bt2L//v14/PgxmjVrhv79+2PWrFlo0qRJrWsMWnIc\nT1/k1/o4VPeiV/gIMm9y8hNs2bIBY8d+DGPj95CSkixIHUREQmMwU3J+fn4wNDTE+vXrMWbMGNjY\n2KBly5ZYv359lY8xd+5cREdHY+jQoRgzZgwePnyIn3/+Gffu3cO2bdugpqZWhx0QASEh3+K990zg\n7x8odClERIJiMFNyDg4OKCoqwvr169GlSxe4u7sDQJWD2dWrV3HgwAGsXr0a/fv3l43b2tpi5syZ\n+Pe//41evXrVSe1EAHDsWAx++eUy1qwJg6YmP5KI6N3GT8F33NGjR9GsWTN07txZtlYNADp16gQN\nDQ1cuXKFwewdU59r54qKirB27Sq4uLigXTtz5OX99TNYUPASAFBSUoi8vAw0b94curq6tZpLFdcE\nAuxL2ahqX4Dq9lbffTGYveMePnyIzMxMODk5Vbr91bBG74b09Ox6mys7OxsZGRk4c+YMzpw5U2F7\nVFQUoqKi8Omn0xEQMKrG8xgY6NRrX/WFfSkXVe0LUN3eqtuXurparW94YzB7x5SWyt91V1ZWBgMD\nAyxfXvmdcK1ataqPsugd1bhxYyxevKzCeGZmJlasWIauXbvD23sQ2rZtJ0B1RET1j8FMRenp6eHx\n48cVxpOT5e92Mzc3x+XLl9GpUydoaWnVV3lEAABNTU24urpXGC+/K9PExKTS7UREqorBTEWZmZnh\n7NmzyMjIkP0VgISEBFy7dg3Gxsay/fr374+dO3di48aNmDJlitwxioqKUFRUhKZNa3dadvO8frV6\nPdUfIZ9jRkREDGYqy9fXF9u2bUNQUBB8fX3x/Plz7N69Gx988AFyc3Nl+3Xr1g3Dhg1DaGgofv/9\ndzg5OUFdXR2JiYmIjY1FSEgIunfvXqtanj/PQVmZtLYtiYoqr6cQA2Pj93D+/FWhyyAiqnf8k0wq\nqm3btvjuu++QnZ2Nb7/9FqdOncLy5cthY2NTYd/FixdjwYIFSEtLw4oVK/Cvf/0Lv/76K4YNGwYr\nKysBqiciIno3qUmlUtU6lUGiwzNmyoN9KRf2pVxUtS9AdXsT4q5MnjEjIiIiEgkGMyIiIiKRYDAj\nIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKR\nYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIi\nIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKR0BS6ACISl0eP\nErF16ybcvZuAZ8/SUVJSAkNDIzg59cCIEaPRsmVLoUskIlJZPGOmZEJDQyGRSN66X1JSEiQSCQ4c\nOFAPVZEqefr0KZ4/f4ZevVwxYcIUTJv2GTp37oqoqEgEBQXixYsMoUskIlJZPGNGRHI6deqCTp26\nVBi3s+uIf/4zGDEx0Rg5cowAlRERqT4GMxVlYmKCmzdvQlNT+Le4RYumQpdQJwwMdOplnoLCEmRn\n5dfLXG9iZGQEAMjOzha4EiIi1SX8b21SqNLSUpSWlkJLSwsNGzYUuhwAQNCS43j6QvhgoayiV/hA\niChUWFiI/Px8FBUVIjHxAX78cTUAwMmphwDVEBG9G7jGTMSuXr2KoUOHwtbWFu7u7ti9e3eFfSQS\nCb755hscPHgQHh4esLW1xW+//VZhjdmmTZtgZWWF1NTUCsf4+uuv4eDggIKCAtnY6dOn4e/vD3t7\nezg6OmLy5Ml4+PBh3TVLonP48EF4e7vjo48GYNasKcjJycE//7kYdnYOQpdGRKSyeMZMpO7cuYOg\noCC0aNECU6dORUlJCUJDQ9GiRYsK+8bHxyM2NhYBAQHQ1dWFgYFBhX08PT3x/fff4+jRoxg7dqxs\nvLS0FHFxcejTpw8aNWoEADhw4AC++uor9O7dG59//jlyc3MRHh6OgIAAHDp0iHflvSN69uwNc/P3\nkZ+fjz/+uIPz588iMzNT6LKIiFQag5lIrV69Gmpqati1axcMDQ0BAP3798fAgQMr7JuYmIgjR47A\nwsJCNpaUlCS3j4mJCezs7BAbGysXzK5cuYLnz5/Dy8sLAJCbm4ulS5di5MiRmD9/vmw/T09PeHt7\nY9u2bZg9e7YiW6UqqK/1bK/OZWCgAxubD/5/dCASErzh6+sLTU0pJkyYUG/1KEp9fg/rE/tSLqra\nF6C6vdV3XwxmIlRaWorz58+jX79+slAGAG3btoWzszPOnj0rt3+3bt3kQtnreHp64rvvvkNycjLe\ne+89AEBsbCx0dHTQs2dPAMCFCxeQnZ0NT09PZGT877EITZo0gZWVFa5cuaKIFqma0tPrZ5WZgYHO\na+dq0cIE7dpJ8PPPO/DRRwH1Uo+ivKkvZca+lIuq9gWobm/V7UtdXa3WN7xxjZkIZWRkoKCgAK1b\nt66wrbIAZmpqWqXjenp6AvgrjAF/BcDjx4/Dzc0NWlpaAP46+wYAI0eOhJOTk9z/rl+/LhfW6N1T\nWFiIrKyXQpdBRKSyeMZMBVT17ksjIyPY29sjNjYWQUFBuHz5Ml68eCG7jAkAUqkUALBixQro6+vX\neC5SXs+fP0OLFhXXEV67dhUPHtyHg4OjAFUREb0bGMxESF9fH40aNar0LsgHDx7U6tienp5YunQp\nHj9+jNjYWOjp6aF79+6y7WZmZgAAAwMDdO3atVZzlds8r59CjvOuKigsqdf5QkKW4fnzZ3B07AxD\nQyMUFRXhzp3bOHnyOLS1tTFlyox6rYeI6F3CYCZCGhoacHZ2RlxcHNLS0mTrzO7fv4/z58/X6tge\nHh5YtmwZDh8+jLi4OPTt2xcNGjSQbXd2dkbTpk2xYcMGODo6VnhAbUZGRqVn0t7k+fMclJVJa1W3\n2KjqegoAcHfvj2PHjuDYsRhkZr4AoAYjIyP4+HyEESNGyx40S0REisdgJlJTp07Fv//9b4wYMQL+\n/v4oLS3Fzz//jA8++AB37typ8XENDQ3RsWNHhIWFITc3V+4yJgDo6Ohg/vz5CA4OxtChQ+Hl5YVm\nzZrhyZMnOHXqFNzc3DBz5szatkci5ubWF25ufYUug4joncRgJlJWVlbYvHkzvv32W6xevRpGRkaY\nOnUq0tPTaxXMAMDLywtXr15F8+bN0a1btwrbBw8eDENDQ2zcuBEbN25ESUkJjIyM0KVLFwwYMKBW\ncxMREdHrqUnLV3sT1RFeylQe7Eu5sC/loqp9AarbGx+XQURERPQOYzAjIiIiEgkGMyIiIiKRYDAj\nIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKR\nYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIi\nIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEgkGMyIiIiKRYDAjIiIiEglNoQsgord79Oghjh+PxZUr\nl5CcnITCwiKYmJjC1dUNw4cHoHHjxkKXSERECsAzZiImkUgQGhoq+zo0NBQSiaRKr63OviR+R45E\nYc+enTAxMcXYsR9j8uRpMDdvjbCwHzFx4j9QWFggdIlERKQAPGOm5DZu3Ig2bdrA3d1d6FKoDrm6\numHUqHFo2rSpbGzwYF+Ymprhp5+24PDhQxg61E/AComISBEYzJTIpEmTMH78eLmxjRs3wt3dvUIw\nq2xfobRo0fTtOymZouLSep3Pysq60nE3t3746act+PPP+/VaDxER1Q0GMyWiqakJTc2qvWXV2beu\nBS05jqcv8oUuQ6GiV/gIXQIA4OnTNACAvn4LgSshIiJF4BqzSqSkpGDOnDlwdnaGra0t+vbtiyVL\nlsi2//e//0VQUBAcHBzg4OCAoKAgJCQkyB2jfI3X48eP8cUXX8DR0RGOjo6YM2cO8vPlQ0pRURGW\nLl2Kbt26wcHBARMnTkRqamqFuv6+bkwikSA7OxuRkZGQSCSQSCQIDg6udF8AKCkpwZo1a+Dm5oYO\nHTrA3d0da9euRWmp/NkfiUSCb775BseOHcOAAQPQoUMHDBgwAOfOnavZN5TqRGlpKbZv3wwNDQ30\n7dtf6HKIiEgBxHFKRUTS0tIwbNgw5Obmws/PDxYWFkhOTkZMTAzmzZuHP/74A4GBgdDV1cWECRMA\nALt27UJAQAD27duHtm3byh1v2rRpMDMzw2effYZbt25h37590NfXx+effy7bZ+7cuYiKisKgQYNg\nb2+PS5cuVeky5PLly/H111/DxsYGw4cPBwCYm5u/dv958+YhMjISAwYMgKOjI65evYrVq1cjJSVF\nLngCwC+//IKjR48iICAA2traCA8Px7Rp03D69Gk0b968yt9PqjurV6/A77/fxIQJk2Fu/r7Q5RAR\nkQIwmP1NSEgIMjIyEBERgfbt28vGZ8yYAQBYtWoVSktLsXPnTpiYmAAAvL294enpiVWrVsndRQkA\ntra2WLRokezrzMxM7N+/XxbMEhISEBUVhVGjRmHevHkAgJEjR+Kzzz7DnTt33lirj48PFi9eDDMz\nM/j4vPnSWkJCAiIjI+Hv74+FCxfK5tHR0cGePXsQGBgIKysr2f73799HTEwMzMzMAABdu3aFj48P\njhw5gsDAwDfO9a4wMNARbO5Vq1YhImIv/Pz8MGvWNIUeW8i+6hL7Ui7sS/moam/13ReD2SvKyspw\n8uRJuLu7y4UyAFBTU0NpaSni4+PRt29fWSgDAFNTU/Tt2xenT59GaWkpNDQ0ZNv8/f3ljtOpUyfE\nxcUhJycHTZs2xdmzZwEAo0ePlttvzJgxOHz4sMJ6K59n3LhxcuNjx47Fnj17cO7cOblg5uzsLAtl\nAGBlZYWmTZvi8ePHCqtJ2aWnZwsy7+bNG7B1axi8vAZiypTZCq3DwEBHsL7qEvtSLuxL+ahqb9Xt\nS11drdY3vHGN2SsyMjKQm5uLdu3avXZ7fn4+LCwsKmxr06YN8vLy8OLFC7lxY2Njua91dXUBAC9f\nvgQAPHnyBJqamjA1Na1wPEUqn+fvlzpbt24NTU1NPHnyRG78vffeq3AMPT09ZGVlKbQuqp7yUObp\n6Y3g4PlQU1MTuiQiIlIgBrM69urZs1dJpdJ6rqR61NUr/9EQe92qbOvWMGzdGob+/b0wZ84/X/se\nERGR8uKlzFfo6+ujSZMm+OOPP167vXHjxnjw4EGFbQ8ePIC2tna1F8abmJigpKQESUlJcmez/vzz\nzyq9vqpnTMrnefToEd5//33Z+KNHj1BSUiJ3aVbRNs/rV2fHFkp9P8csImIvNm/eAENDI3Tq1AVx\ncUfltuvr66Nz5271WhMRESkeg9kr1NXV4ebmhiNHjuDWrVuwtv7fQz2lUik0NDTQo0cPxMXFITk5\nWXa5Lzk5GXFxcXBxcXntGbLX6dWrF1auXImffvpJtvgfALZv316l1zdu3LhKlxddXFywcuVKbN++\nHV9//bVs/KeffpJtryvPn+egrEy1zrTV92LQhIRbAIC0tFR8882CCtvt7TsymBERqQAGs7+ZNWsW\n4uPjMXLkSPj7+8PCwgIpKSmIiYnBsWPHMGPGDFy4cAEBAQEYMWIEgL8el6GhoSG7c7M62rdvD29v\nb4SHhyMrKwt2dna4dOkSHj58WKXX29jY4OLFi9i6dStatWoFU1NT2NnZVdjPysoKQ4YMwc6dO5GV\nlYWOHTvi2rVrOHz4MHx9ffl3NUVu7twFmDt3gdBlEBFRHWMw+xtjY2Ps3bsXq1atQmRkJHJzc2Fs\nbIzevXsDANq1a4eff/4ZK1aswPr16wEAHTt2xOzZsys8w6yqli5diubNmyM6OhpxcXHo2rUrNm7c\nWKWzWF9++SXmz5+PVatWoaCgAEOGDKk0mAHAkiVLYGpqigMHDuDYsWNo1aoVpk2bhokTJ9aobiIi\nIlIsNSlXc1MdU9VLmbw1XHmwL+XCvpSPqvbGx2UQERERvcMYzIiIiIhEgsGMiIiISCQYzIiIiIhE\ngsGMiIiISCQYzIiIiIhEgsGMiIiISCQYzIiIiIhEgsGMiIiISCQYzIiIiIhEgsGMiIiISCQYzIiI\niIhEgsGMiIiISCQYzIiIiIhEgsGMiIiISCQYzIiIiIhEgsGMiIiISCQYzIiIiIhEgsGMiIiISCQY\nzIiIiIhEgsGMiIiISCQYzIiIiIhEgsGMiIiISCQYzIiIiIhEgsGMiIiISCQYzIiIiIhEgsGMiIiI\nSCQYzIiIiIhEgsGMiIiISCQ0hS6AVJ+6uprQJdQJ9qVc2JdyYV/KR1V7q05fivgeqEmlUmmtj0JE\nREREtcZLmUREREQiwWBGREREJBIMZkREREQiwWBGREREJBIMZkREREQiwWBGREREJBIMZkREREQi\nwWBGREREJBIMZkREREQiwWBGREREJBIMZkREREQiwWBGCldUVITvv/8ezs7O+PDDDzF8+HBcvHhR\n6LJq5ebNm1i4cCG8vLxgb2+P3r17Y+bMmXj48KHQpSlcWFgYJBIJfHx8hC6l1m7evInx48ejc+fO\ncHBwwKBBg3DgwAGhy6qVxMREzJgxA7169YK9vT28vLywceNGFBUVCV1alT19+hQhISEYNWoUHBwc\nIJFIcPny5Ur3PXnyJIYMGQJbW1v07t0ba9asQUlJST1XXDVV6evFixfYtGkTAgIC0K1bN3Tq1Al+\nfn6IjY0VqOq3q877Ve7Jkyews7ODRCLB7du366nS6qlOX9nZ2Vi2bBlcXV3RoUMHuLi4YNasWXVS\nl2adHJXeacHBwTh+/DhGjx6N1q1bIzIyEp988gnCw8Ph4OAgdHk1smnTJly7dg0eHh6QSCRIT0/H\njh07MHjwYOzfvx9t27YVukSFSE9Px48//ghtbW2hS6m1s2fPYvLkyejSpQumT58OTU1NJCYmIiUl\nRejSaiwtLQ3Dhg2Djo4OAgMDoaenh6tXr2LFihX4448/8P333wtdYpU8ePAAYWFhaN26NSQSCX77\n7bdK9yt/D7t164b58+fj7t27WLt2LV68eIH58+fXc9VvV5W+rl+/jlWrVqFXr16YNGkSNDU1cezY\nMcyYMQN//vknJk+eLEDlb1bV9+tV3333HdTVxX3up6p9ZWVlYeTIkcjKysKwYcNgZGSE9PR0/PLL\nL3VTmJRIgW6p0dOVAAAPEUlEQVTcuCG1tLSUbt26VTZWUFAgdXd3lwYEBAhXWC39+uuv0sLCQrmx\nBw8eSDt06CD98ssvBapK8b788kvpqFGjpIGBgdJBgwYJXU6NZWVlSZ2cnKSLFy8WuhSF2rBhg9TS\n0lJ69+5dufGpU6dKra2tpUVFRQJVVj3Z2dnSjIwMqVQqlcbFxUktLS2lly5dqrCfl5eXdMiQIdKS\nkhLZ2MqVK6VWVlbSBw8e1Fe5VVaVvh49eiRNSkqSGysrK5OOHj1a+uGHH0rz8/Prrd6qqur7Ve7S\npUtSGxsb6cqVK6WWlpbSW7du1Vep1VLVvubPny/t06ePbN+6Ju44S0rn6NGjaNCgAYYNGyYba9iw\nIXx9ffHrr7/i6dOnAlZXcx07doSWlpbc2Pvvv4927drh/v37AlWlWDdv3kRUVBTmzJkjdCm1Fh0d\njaysLEyfPh0AkJOTA6lUKnBVtZebmwsAaNGihdx4y5YtoampCQ0NDSHKqramTZuiefPmb9zn3r17\nuHfvHvz8/OT6CggIQFlZGY4fP17XZVZbVfoyMzODiYmJ3Jiamhrc3d1RUFCAJ0+e1GWJNVKVvsqV\nlpbim2++QWBgIFq3bl3HldVOVfrKyspCZGQkgoKC0Lx5cxQWFtb5sgEGM1Ko27dvw8LCAk2aNJEb\n//DDDyGVSkW71qAmpFIpnj17VuUPLDGTSqVYvHgxBg8ejPbt2wtdTq1dvHgRbdq0wdmzZ+Hi4gJH\nR0d06dIFISEhKC0tFbq8GuvcuTMAYO7cuUhISEBKSgqioqJkywXEfumoOm7dugUA6NChg9y4oaEh\njIyMZNtVxbNnzwBA6T9Pdu/ejbS0NHz66adCl6IQV69eRVFREVq2bImxY8fCzs4O9vb2+Mc//oFH\njx7VyZxcY0YKlZ6eDkNDwwrjBgYGAKC0Z8wqExUVhbS0NMycOVPoUmrt4MGDuHfvHtauXSt0KQrx\n8OFDpKamIjg4GB9//DGsra1x+vRphIWFobCwEHPnzhW6xBpxdnbG9OnTsWHDBpw6dUo2Pm3aNFGu\nTaqN9PR0AP/77HiVgYGBSn2WZGZmYt++fejSpQv09fWFLqfGMjMzsXr1akydOhW6urpCl6MQ5eFr\n/vz56NChA1auXImnT59izZo1GDNmDKKjo9G0aVOFzslgRgpVUFCABg0aVBhv2LAhAKCwsLC+S6oT\n9+/fx6JFi+Do6Kj0dy/m5ORgxYoVGD9+PFq1aiV0OQqRl5eHly9f4rPPPsP48eMBAP369UNeXh52\n7dqFSZMmKe0vQFNTU3Tp0gV9+/ZFs2bNcObMGYSGhkJfXx8jRowQujyFKSgoAIAKSwiAvz5P8vPz\n67ukOlFWVobZs2cjOzsb8+bNE7qcWlm9ejX09fXh7+8vdCkKU758wMDAAGFhYbKz0hYWFhg/fjwi\nIiIwZswYhc7JYEYK1ahRIxQXF1cYLw9k5QFNmaWnp2PChAnQ09PDv/71L6W/fPTjjz+iQYMGGDdu\nnNClKEyjRo0AAN7e3nLjAwcOxNGjR/Gf//wHLi4uQpRWK0eOHMHXX3+No0ePys5M9+vXD1KpFMuX\nL4eXlxf09PQErlIxyt/DytbzFBYWyrYru8WLF+P8+fMICQmBRCIRupwau3v3Lnbv3o0ff/wRmpqq\nEy3Kf848PDzkPutdXFygp6eHa9euKTyYKfdvFBKd111iKL8soexnZLKzs/HJJ58gOzsbmzZtqvQy\nizJ5+vQptm/fjoCAADx79gxJSUlISkpCYWEhiouLkZSUhJcvXwpdZrWVvy8tW7aUGy//Whl7AoCd\nO3fCxsamwnKBPn36IC8vDwkJCQJVpnjl72H5Z8er0tPTlf6zBADWrFmDnTt34vPPP6/wjwhls3Ll\nSlhbW6Nt27ayz5EXL14A+OtzRlkfU/O6zxIA0NfXR1ZWlsLnVJ1YS6JgZWWF8PBw5Obmyt0AcOPG\nDdl2ZVVYWIiJEyciMTER27ZtQ5s2bYQuqdaeP3+O4uJihISEICQkpMJ2Nzc3fPLJJ5g9e7YA1dWc\njY0NLly4gLS0NJiZmcnGU1NTAUBpL2M+e/as0trLz1Ir840Nf1d+E8rvv/8OGxsb2XhaWhpSU1OV\n/iaVHTt2IDQ0FGPHjkVQUJDQ5dRaSkoKEhIS4ObmVmHb+PHj0bJlS8THxwtQWe2U/+ylpaXJjZeV\nlSE9PV3uZ1NRGMxIoTw8PLBlyxbs27cPY8eOBfDXpYgDBw6gY8eOld4YoAxKS0sxY8YMXL9+HevW\nrYO9vb3QJSmEqalppQv+V61ahby8PHz11Vd4//3367+wWvLw8EBYWBj2798vuzlDKpVi37590NbW\nVtr3z8LCAvHx8Xj06BHMzc1l40eOHIGGhoZSXwr7u3bt2qFNmzbYs2cPfH19ZY/M2LVrF9TV1dGv\nXz+BK6y5mJgYLFmyBAMHDkRwcLDQ5SjEnDlzkJOTIzd26dIlhIeHY86cOUr7D9m2bdvC0tIS0dHR\nmDhxomw5TkxMDHJycuDk5KTwORnMSKHs7Ozg4eGBkJAQpKenw9zcHJGRkUhOTsa3334rdHk1tmzZ\nMpw6dQqurq7IzMzEoUOHZNuaNGkCd3d3AaurOR0dnUpr3759OzQ0NJS2rw4dOmDw4MHYsGEDnj9/\nDmtra5w9exbnz5/H559/rvC7qOpLUFAQzp07hxEjRmDkyJHQ09PDmTNncO7cOfj7+1d4vpmYrVu3\nDgBkzwE8dOgQfv31V+jq6iIwMBAA8MUXX2DSpEkICgqCl5cX7t69ix07dsDPzw8WFhaC1f4mb+vr\n5s2b+OKLL9CsWTM4OTkhKipK7vU9evSo9LKZ0N7WV7du3Sq8pvwyX9euXUV7hrMqP4fBwcH45JNP\nEBAQAB8fH6Snp2P79u2wtrbGoEGDFF6TmlQVnrpIolJYWIhVq1YhOjoaL1++hEQiwaxZs9C9e3eh\nS6uxUaNG4cqVK5VuMzExkXt0gSoYNWoUsrKy5AKosikqKsK6detw8OBBPHv2DKamphg7dqzS3zF2\n8+ZNhIaG4vbt28jMzISJiQmGDh2KoKAgpXnALIDXnt37+39PJ06cwJo1a3D//n3o6+tj6NCh+PTT\nT0W7wPxtfR04cOCND3H+6aef0LVr17oqr8aq+n69qrzXgwcPijaYVbWvc+fOITQ0FHfu3IG2tjbc\n3Nwwe/bsOnnuHIMZERERkUjwrkwiIiIikWAwIyIiIhIJBjMiIiIikWAwIyIiIhIJBjMiIiIikWAw\nIyIiIhIJBjMiIiIikWAwIyIiIhIJcT46mYhIBVy+fBmjR49+7fY9e/Yo7d/tJKK6wWBGRFTHvL29\n0atXrwrjr/4hciIigMGMiKjOWVtbw8fHR+gyai0nJ0dp/wA8kbJgMCMiEqnCwkJs3LgRhw8fRmpq\nKho0aABjY2M4Ozvjyy+/lNv30qVL2LJlC27cuIG8vDy0atUKXbt2xezZs6Gvrw8AKCkpwZYtW3Dw\n4EE8fvwY2tra6NSpE6ZNmyb3x5yTkpLg5uaGKVOmoG3btti0aRPu3bsHLy8vLFu2DADw9OlTrF27\nFmfPnsWzZ8/QrFkzuLq6YsaMGWjRokX9fZOIVAyDGRFRHcvPz0dGRobcmJaW1lvPPi1cuBAREREY\nPHgwHBwcUFpaisTERFy+fFluv927d2PBggUwNDSEv78/TExMkJycjNOnTyMtLU0WzGbPno3Y2Fj0\n6NEDI0aMwLNnz7Bjxw74+/tjx44dsLa2ljvuiRMnEB4ejhEjRsDf319Wb3JyMvz8/FBcXAxfX1+Y\nm5vj4cOH2LVrFy5fvoyIiAjo6OjU9ttG9E5Sk0qlUqGLICJSRW9a/O/l5YUffvjhja/v0qUL7Ozs\nEBYW9tp9UlNT4e7uDnNzc+zevRu6urpy28vKyqCuro74+Hj84x//gKenJ3744QeoqakBABISEvDR\nRx/B3t4eO3fuBPC/M2aampqIiopC27Zt5Y45adIkXL9+HZGRkTAyMpKN/+c//4Gfnx8mTZqEqVOn\nvrE3Iqocz5gREdUxPz8/eHh4yI21bNnyra9r2rQp7t27h7t378LS0rLSfY4ePYri4mJMmTKlQigD\nAHX1v56KFBcXBwCYOHGiLJQBgJWVFVxdXXHixAlkZGTIzq4BgIuLS4VQlp2djTNnzuCjjz6ClpaW\n3JlAExMTmJubIz4+nsGMqIYYzIiI6ljr1q3RvXv3ar/uq6++whdffIGBAwfCzMwMXbt2haurK/r0\n6SMLXImJiQCA9u3bv/FYSUlJUFdXrxC0AOCDDz7AiRMnkJSUJBfM3n///Qr7PnjwAGVlZdi/fz/2\n799f6VxmZmZV7JCI/o7BjIhIpNzd3XHq1CmcPXsWv/zyCy5cuID9+/ejU6dO2Lp1K7S0tOp0/saN\nG1cYK1/9MmjQIAwZMqTS1zVs2LBO6yJSZQxmREQi1qxZM/j4+MDHxwdSqRQhISHYtGkTTp48CU9P\nT9lZrdu3b8PCwuK1xzEzM0NZWRnu378PKysruW33798HAJiamr61HnNzc6ipqaG4uLhGZwGJ6M34\nJ5mIiESotLQUWVlZcmNqamqyOydfvnwJAPDw8ECDBg2wdu1a5OTkVDhO+Rkud3d3AMDGjRvx6j1f\nd+/exalTp+Do6Ch3GfN1mjdvDhcXF8TFxeH69euVzvf3O1CJqOp4xoyISIRyc3Ph7OyMPn36wNra\nGvr6+khKSsKuXbugp6cHV1dXAICRkRG++uorLFq0CAMHDoSPjw9MTEyQlpaGkydPYunSpWjfvj16\n9OgBT09PHDlyBC9fvoSrqyvS09Oxc+dONGzYEPPmzatybQsWLEBAQAACAwPh4+MDa2trlJWV4fHj\nxzh58iQGDx7Mxf9ENcRgRkQkQo0aNcKYMWNw8eJFXLx4Ebm5uWjVqhX69OmDCRMmwNDQULZvQEAA\nzM3NsXnzZoSHh6OoqAitWrWCk5OT3OMsQkJCYG1tjcjISCxbtgza2tro3Lkzpk+fLveA2bcxNjZG\nREQEwsLCcOrUKURFRaFhw4YwNjaGq6srPD09Ffq9IHqX8DlmRERERCLBNWZEREREIsFgRkRERCQS\nDGZEREREIsFgRkRERCQSDGZEREREIsFgRkRERCQSDGZEREREIsFgRkRERCQSDGZEREREIvF/E7mH\nPDkAU5gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5YWs53neoTUi",
        "colab_type": "code",
        "outputId": "f1b28209-34b2-41e3-9cb4-c05e314bd92e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        }
      },
      "source": [
        "import seaborn as sns\n",
        "xgb.plot_importance(xg_reg, importance_type= 'gain')\n",
        "sns.set(font_scale=1.1)\n",
        "plt.rcParams['figure.figsize'] = [6,6]\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m5otRokRJpk//ghIlnDEyMiIm5ipjxgzDycmJ+vUbAbB//zGDfZYsWUBExCkq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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}